Anonymous communication is a key privacy-enhancing technology that aims to protect user identity in online communications [19, 26, 8, 5, 29]. The most widely-used anonymity protocol today is onion routing , which in the form of the Tor network 
is estimated to have over 2 million users a day. The Tor network comprises overvolunteer proxies, carries 100 Gbps of traffic, and is widely used by citizens, journalists, whistleblowers, businesses, governments, law-enforcement, and intelligence agencies [63, 61]. An important thread of research has proposed new anonymity systems that improve on Tor in the context of network-level adversaries, such as Autonomous Systems (ASes) that have vast visibility into Internet traffic. Systems such as DeNASA, Astoria, TAPS, and Counter-RAPTOR have modified path-selection algorithms for onion routing to mitigate the threat of AS-level adversaries [4, 50, 36, 58]. Systems such as LAP, Dovetail, PHI, and HORNET have moved cryptographic functionality for anonymous communication from end hosts into the Internet routing infrastructure to improve performance [31, 56, 11, 12].
However, for simplicity of security analysis, designers of these systems abstract away important components of the system, which could impact user anonymity in practice. In particular, one simplification commonly used in the analysis of anonymity protocols is to limit the effects of time on the operation of the protocol [1, 50, 4, 31, 56]. For example, security analyses typically assume that each user communicates with a fixed destination once, that the set of participants in the protocol is static, or that the network structure is static. The question then arises: what are the effects of the temporal dimension of system operations on user anonymity?
Contributions. In this paper, we present Tempest: a set of attacks that demonstrates the impact of temporal dynamics on the security of several prominent anonymity protocols. We target Tor and some of the latest proposals for improving its security against AS-level adversaries (namely, DeNASA , TAPS , and Counter-RAPTOR ), as Tor has proven to be the most popular protocol for the current Internet. We also target proposals for network-layer anonymity that represent the main ideas for providing anonymity against AS-level adversaries in a next-generation Internet (namely, Dovetail  and HORNET ).
|Exploiting Client Mobility||Exploiting User Behavior||Exploiting Routing Changes|
|Vanilla Tor ||Novel (§5.2)||Known [7, 30, 38, 34]||Known |
|DeNASA ||Novel (§A.1)||Novel (§6.1)|
|Counter-RAPTOR ||Novel (§5.3)||Novel (Resistance, §B.1)||Known (Resistance) |
|TAPS ||Known (Resistance) ||Novel (§7.1)|
|Astoria ||Known |
|HORNET ||Novel (§5.4)||Novel (§7.2)|
|Dovetail ||Novel (§6.2)|
|PHI ||Novel (§B.2)|
|LAP ||Novel (implied by §5.4)||Novel (implied by §7.2)|
We consider the vulnerability of such protocols to deanonymization due to the effects on anonymous-communication paths of three main types of temporal dynamics: (1) client mobility, (2) user behavior over multiple connections, and (3) network routing dynamics. We consider especially a patient adversary that is interested in performing long-term attacks on anonymous communication. Such an adversary is a real concern of today’s Tor users [38, 33], for example those avoiding mass surveillance. We propose and, using real-world datasets, evaluate attacks that allow an adversary to exploit long-term observations about anonymity path changes for deanonymizing user identities.
We find that Tempest attacks have significant impact on the anonymity of these systems. One impact that we show is that, in Tor-based systems, path changes due to client mobility allow an increasing number of AS-level adversaries to observe client traffic, compromising client identity with a degree much greater than previously thought possible. In network-level anonymity systems, adversaries can also correlate partial information about anonymity paths with auxiliary information about client movements to deanonymize client identity. Our work presents the first analysis of the impact of path changes due to client mobility. Another impact that we show is that path changes due to user behavior, such as multiple connections to the same destination at different times, and path changes due to network routing updates allow an adversary to combine probabilistic information leaks inherent in path-selection algorithms to deanonymize clients’ ASes over time. Note that inferring the AS of a client represents a significant reduction in client anonymity (the typical anonymity set comprises over 50,000 ASes without our attacks). Our work is the first demonstration of how probabilistic information leaks due to the restricted AS-level Internet topology can be aggregated over time.
Our results present a new evaluation paradigm for important classes of anonymous-communication protocols. They suggest that designers of anonymity systems should thoroughly consider the impact of temporal dynamics when analyzing system security. Our work further motivates the design of anonymous communication protocols that are resilient when used over time and under changing circumstances.
2 Overview of Tempest Attacks
In this section, we provide an overview of Tempest attacks and summarize our key findings.
Exploiting Client Mobility. We demonstrate how an adversary can exploit information leakage via naturally-occurring real-world movements of clients. Client mobility results in connections to anonymity networks appearing from different network locations over time; we find that this enhances an adversary’s ability to perform traffic-analysis attacks and deanonymize client communications. We experimentally quantify the degradation in anonymity for Vanilla Tor (i.e. plain Tor as it exists today), Counter-RAPTOR , and HORNET  using real-world location datasets to model client mobility. Across all studied systems, we find that considering the effects of client mobility results in an order-of-magnitude degradation in client anonymity: (1) for Vanilla Tor, client mobility increases the exposure of the client-Tor communications to AS-level adversaries, due to heterogeneous network paths originating from varying client locations; (2) for Counter-RAPTOR, client mobility increases an adversary’s ability to actively manipulate BGP routing and hijack/intercept client traffic to the anonymity network, due to a fundamental mismatch in assumptions between its location-aware path selection and the dynamics of client mobility; and (3) for HORNET, client mobility results in changes in the network paths between a client and its destination over time, which can be correlated with external (non-anonymous) location datasets to deanonymize the anonymity-network connections.
Exploiting User Behavior. We consider users that regularly connect to a destination and demonstrate how an adversary can take advantage of that to deanonymize users in several prominent anonymity proposals. Multiple user connections allow an adversary to aggregate probabilistic information leakage from connections over time and eventually deanonymize the user by identifying his AS. We experimentally quantify this degradation in anonymity over multiple connections for DeNASA , a location-aware path selection algorithm for Tor that avoids suspect ASes, and Dovetail , a network-level anonymity protocol that uses a level of indirection within Internet communications to provide anonymity. We find that multiple connections have devastating consequences on user anonymity in DeNASA and Dovetail. The path selection algorithms in both DeNASA (focusing on the first-hop/guard relay) and Dovetail leak partial information about the client’s network location, leading to a continual reduction in anonymity as the client makes connections. The speed of this reduction is surprisingly fast for some unfortunate clients.
Exploiting Routing Changes. We show how an adversary can exploit naturally-occurring routing changes to compromise client anonymity. Similar to the impact of client mobility, routing changes leak additional information to an adversary as they occur, which can be aggregated over time to make accurate inferences about client location. We experimentally quantify the degradation in anonymity due to routing changes for TAPS , a trust-aware path selection algorithm for Tor, and HORNET . For both TAPS and HORNET, routing changes lead to varying anonymity sets for clients over time, allowing an adversary to intersect the anonymity sets at different points in time and infer client network locations (client ASes).
Impact. The impact of the Tempest attacks is summarized in Table 1. For each of the three temporal dynamics considered, we demonstrate attacks that weaken security in at least one onion-routing protocol and one network-layer anonymity protocol. We note that our results on exploiting client mobility represent the first analysis of this issue, to the best of our knowledge, and we demonstrate its negative impact on a range of systems. We also note that our results on exploiting user behavior and routing changes include several novel attacks across recently-proposed onion-routing and network-layer anonymity protocols, suggesting a significant re-evaluation of their effectiveness. In particular, our work is the first to consider the aggregation of probabilistic information leaks due to the restricted AS-level Internet topology over time.
To highlight the broad impact of Tempest, Table 1 includes novel attacks that appear in the Appendix, including exploiting client mobility in DeNASA, exploiting user behavior in Counter-RAPTOR, and exploiting user behavior in PHI (note that the results in the main body of the paper are self-contained). Table 1 also includes entries for protocols not studied directly in this work. It places into context an attack  on Astoria  that is similar to our attacks exploiting user-behavior dynamics. It also indicates that some of our attacks should also be effective against LAP , as LAP reveals strictly more to the adversary about the source and destination of a connection than HORNET does.
Due to the variety of systems considered, the Tempest attacks vary in the kind of anonymity degradation they achieve and in the adversary capabilities that they require. In several cases we attack anonymity using the same notions and metrics used to argue for the system’s effectiveness by its designers. In every case, the adversaries we consider fall within the threat model stated for the system under analysis. In addition, the adversaries we consider are generally passive and need to control only one or a small number of network entities (e.g. an AS, a website, or a Tor relay). To summarize our contributions, we identify the effects of temporal dynamics on paths in anonymity systems as a general concern affecting the anonymity of those systems. We present the Tempest attacks and show that they can reduce the anonymity of Tor, suggested Tor improvements, and network-layer anonymity protocols.
In this section, we present the required background on Internet routing and anonymity protocols.
Network Routing. Routing in the Internet is set up among routers via the Border Gateway Protocol (BGP). BGP produces routing paths between the autonomous subnetworks that comprise the Internet, called Autonomous Systems (ASes). There are roughly ASes advertising routes on the Internet . Each AS is connected to at least one other AS, and the connected ASes exchange traffic with each other in a variety of bilateral relationships that specify when traffic should be sent and how it is paid for. In BGP, routing operates on variable-length IP prefixes, which are each a sequence of bits that is compared to beginning of the destination IP address to route a packet.
Onion Routing. Onion routing  achieves anonymous communication online by encrypting the network traffic and sending it through a sequence of relays before going to the destination. The relays run at the application layer on the hosts, and traffic between each pair of hosts is routed using existing Internet routing protocols. To communicate, the client selects a sequence of relays, constructs a persistent circuit through them, and uses it to establish a connection to the destination. The circuit is constructed iteratively and is encrypted once for every relay, which prevents each relay from learning more than the previous and next hops, and in particular it prevents any one relay or local network observer from identifying both the source and destination. Servers can remain anonymous by running as hidden services, which maintain persistent circuits into the anonymity network through which they can be contacted. Onion routing is well-known to be vulnerable to a traffic correlation attack , however, in which an adversary that observes both the client and the destination can deanonymize the connection by correlating the traffic leaving the client with that entering the destination.
Tor  is the most popular system implementing onion routing. The Tor network currently consists of over relays cumulatively forwarding 100 Gbps of traffic . We will apply Tempest attacks to Tor as well as to several recent proposals to improve Tor’s security by changing the way that it selects paths: DeNASA , TAPS , and Counter-RAPTOR . These attacks are likely to also be effective for other similar proposals [20, 37, 1, 39, 50, 3].
Network-Layer Anonymity Protocols. Onion routing protocols are run at the application layer, which allows them to be deployed without changes to the existing Internet. However, several protocols [31, 56, 11, 12] propose operating at the network layer for efficiency and ubiquity. These protocols change the way that routers set up and route packets and thus require changes in some of the core infrastructure of the Internet. Several of them make use of some other next-generation routing algorithm (e.g. pathlets ) to propagate routing information and select routing paths. These protocols have many similarities with onion routing, and it is also useful to view them through the temporal lens. We focus on two of these protocols, HORNET  and Dovetail , because they represent two distinct approaches that have been suggested. As indicated in Table 1, our results also imply vulnerability in the other network-layer anonymity protocols.
4 Models and Metrics
|System||Adversary||Supporting Capabilities||Attack Goal||Metric|
|§5.2||Vanilla Tor||Single AS||Observe client
|Probability of observing
|§5.3||Counter-RAPTOR||Single AS||BGP hijack||Observe client
|Probability of hijacking
|§5.4||HORNET||Destination AS||Links client connections
Has identified mobility dataset
|Link pseudonym to real-world identity||Accuracy and rejection rate when guessing client identity|
|§6.1||DeNASA||Some Tor relays||Links client connections||Identify client AS||Entropy of posterior
distribution over ASes
|§6.2||Dovetail||Single AS||Links client connections||Identify client AS||Set size of possible client ASes|
|Induces linkable circuits
Performs guard discovery
|Identify client AS||Set size of possible client ASes|
|§7.2||HORNET||Destination AS||Tracks connection across
|Identify client AS||Set size of possible client ASes|
|§A.1||DeNASA||Single AS||Observe client
|Probability of observing
|§B.1||Counter-RAPTOR||Some Tor relays||Links client connections||Identify client AS||Entropy of posterior
distribution over ASes
|§B.2||PHI||Single AS||Links client connections||Identify client AS||Accuracy and rejection rate when guessing client AS|
Overview of Tempest attacks showing the attacked system, adversary capabilities, attack goal, and evaluation metric.
The Tempest attacks each exploit some temporal dynamic, but they differ in where and how it is exploited. An overview of the attack methods appears in Table 2.
Adversary Models. Motivated by mass-surveillance concerns, we consider the context of a patient adversary that is interested in performing long-term deanonymization attacks. We consider several different adversaries, depending on the system and the way a temporal dynamic enables an attack. In each case we identify a fairly weak adversary within the attacked system’s threat model, with results in the following threat models: a single AS, at least one Tor relay, and the destination site. Our adversaries are generally passive, with the exceptions that we consider an active IP prefix hijack (Section 5.3) and that active methods may be used to link together connections as originating from the same client.
In several of our attacks, we assume that the adversary has this ability to link together multiple connections (see Table 2). Observations at the Tor-protocol level can allow circuits to be linked via timing [30, 32], but the application layer enables even more effective linking in many important use cases, including (1) a malicious website, which can either passively observe pseudonymous logins or actively create linkable connections ; (2) a public IRC channel, which makes pseudonymous activity observable by any adversary ; (3) a hidden service that a malicious client can repeatedly connect to ; and (4) an administrative service that only one entity has access to, such as SSH access to a personal server, where such accesses could be observed and recognized by a malicious ISP hosting the server.
Anonymity Metrics. Due to the diversity of our attacks, we use several types of metrics for their evaluation (see Table 2 for each attack’s metric).
Probability of observing the client connection: In some attacks, the adversary attempts to observe the traffic between the client and the anonymity network. Such observations can facilitate attacks like website fingerprinting  and timing analysis . We quantify this attack as the probability that the adversary succeeds in observing the client connection.
Size of source anonymity set: In cases where the adversary uses his observations to reduce the set of possible sources, we measure anonymity as the size of this set . The sources in our attacks are ASes.
Accuracy and rejection rate when guessing source
: Some attacks score possible sources heuristically and then guess the highest-scoring source (sources are ASes in our attacks). Because these methods aren’t perfect, the guess may be wrong. However, we can recognize when the scores are too low to make a confident guess and reject making one. For this approach (multi-class classification with the reject option) we measure anonymity using accuracy and rejection rate . Accuracy is the fraction of correct guesses among cases in which a guess is made, and rejection rate is the fraction of cases in which no guess was made.
Entropy of source distribution
: When the adversary’s observations allow him to perform Bayesian inference on the source, we measure anonymity as the entropy of the posterior distribution. Our distributions are over ASes, and we use a uniform prior. Bayesian inference is a strong deanonymization technique, but it is not feasible for all attacks (e.g. due to computational constraints), which prevents us from using this metric in many cases it might otherwise apply.
Note that many of our metrics measure anonymity of the client AS. Although a single AS may serve many thousands of clients, an attack that identifies the client AS is still quite dangerous, as (1) the client AS can be targeted to divulge the user’s real identity (such targeting would likely be necessary for real-world deanonymization even if the client’s IP address were known); (2) the diversity of user attributes (e.g. physical location) is much lower within a client AS and may combine well with auxiliary knowledge; and (3) the client AS can be used to link connections and build a pseudonymous profile. Indeed, a main challenge in designing network-layer anonymity protocols is hiding the client AS, as the IP address can be easily hidden by the client AS using Network Address Translation.
In several analyses, we focus on the anonymity of the users against whom the attack is most successful, such as those in locations that experience the largest anonymity losses. The vulnerability of such users to deanonymization is important to consider, as (1) users don’t benefit equally from anonymity, and the most vulnerable users may suffer the most from deanonymization; (2) even if a minority of users will end up being deanonymized, that small risk may be too high for a majority of users, who thus cannot use the system; and (3) relatively few deanonymizations may erode overall trust in the system given the difficulty of communicating inconsistent anonymity guarantees.
Network Model. To model Internet routing, we generally infer an AS-level topology. We infer AS paths using the algorithm proposed by Mao et al. , which searches for shortest valley-free paths that respect local preference for different economic relationships (e.g. customer, provider, peer). While this type of inference isn’t perfect , it is used in the original evaluations of all the recently-proposed anonymity systems that we study (Tor, being an older design, originally omitted any network-level analysis at all but has since been evaluated using traceroute data ). This is true even for those systems (HORNET, Dovetail) that do not work with BGP, which are evaluated using the existing Internet topology under the supposition that a future Internet would have similar topological properties.
5 Client Mobility
We demonstrate how an adversary can exploit information leakage from client movements to deanonymize client communications. As clients connect to anonymity networks from various network locations, they expose themselves to more adversaries and leak location data that will enable adversaries to deanonymize them. We experimentally quantify the effectiveness of such attacks for Vanilla Tor, Counter-RAPTOR, and HORNET using real-world Foursquare and Gowalla data to model client mobility. We also present supplemental results for DeNASA in Appendix A.1.
5.1 Mobility Dataset
We explored two datasets to model client movements: a Gowalla Dataset  and a Foursquare Dataset [67, 66]. The datasets contain location data from real users over the periods Feb 2009–Oct 2010 and Apr 2012–Sep 2013, respectively. We focus on the country-level movements of users and use it as a proxy for network movements, as we lack fine-grained data to model AS-level movements.
Table 3 compares the two datasets in terms of numbers of users with distinct country-level movements. Note that we only count new countries in the number of countries visited, i.e.
, if a user moves out of a country and later travels back, we do not recount it. The table also shows the Quartile 1 () and median number of days it takes to visit each number of countries in both datasets. The median time may take months due to some infrequent travelers, but there are some clients who visit several countries in less than a month. We also notice that the Foursquare dataset shows a higher /median number of days, indicating that Foursquare users travel less frequently than Gowalla users. However, given the large number of Foursquare users, the absolute number of users for a given travel frequency is similar between the two datasets.
For our analysis in this section, we use both the Foursquare and Gowalla datasets to model client movements. We use two datasets to map each country to a “possible” AS that a user may connect from in that country: (1) for evaluations on the Tor network, we use Juen’s top Tor client ASes dataset  to map the country to the top Tor client AS located in that country, and (2) for network-layer anonymity protocols, we use the top Internet Service Provider per country (based on the number of active IP addresses) .
5.2 Vanilla Tor
Protocol. A Tor circuit typically consists of three hops. Clients choose a small set of relays (the default number is one) called guards that are used as the first hop in every circuit. A client will attempt to use the same guard for four to five months before choosing new guards, but this rate is often accelerated because a client is forced to choose a new guard if its guard goes offline. To balance the traffic load, Tor relays are chosen by clients with probability proportional to their bandwidth.
Attack. We quantify the probability that, as a client moves, an adversary is able to observe the client’s Tor traffic from the critical position between the client and its guard. Such a position allows the adversary to observe the client IP address and thus to perform website fingerprinting , to locate hidden services , and to deanonymize via traffic correlation when destination observations are also available . This position is so sensitive that Tor developed entry guards specifically to make it difficult to observe. We consider a passive adversary controlling a single AS whose goal is to be on-path between a client and its guard relay at some point in time. As a client moves to new locations while still using the same guard relay chosen at the initial location, an AS-level adversary will have increasing probabilities to observe the client-guard connection. Note that, although we suppose that the client uses the same guard across movements (e.g. by using Tor on a laptop), a similar increase in client-guard traffic exposure occurs if the client uses a different Tor instance at each new location.
Methodology. We obtain Tor network data from CollecTor . We use a Tor relay consensus file from 1 Oct 2016 and retrieve relevant data fields from each relay entry, such as guard flag, bandwidth, and IP address. We map IP addresses to ASes using Route Views prefix-to-AS mappings . We use CAIDA’s Internet topology  with inferred AS relationships from Oct 2016. We use the same data to model Tor and Internet routing throughout the paper unless otherwise noted.
We evaluate the CAIDA top 50 ASes  as potential adversaries which include all Tier 1 ASes. We choose the top 50 ASes to evaluate because they are large Internet service providers that carry a significant amount of network traffic, and thus they are at a good position to observe client-guard connections (we did also consider the attack from all ASes and observed a similar increase in probability). We measure the probability of these ASes to be on-path in the client-guard connection at least once during the client’s movements. We assume that the client connects to the Tor network at least once in each country. For a given client and for each guard, we use AS path inference to determine if an adversary AS is on-path at the client’s current and previous locations. We then compute the probability by weighting across the guards’ bandwidth.
Figure 1 shows the probability of compromising a client-guard connection in Vanilla Tor, averaged over the top 50 ASes. Each point on the line shows the median attack probability over clients with a given number of country-level movements. The shaded area shows values between , where and are Quartile 1 and Quartile 3, respectively, and IQR, the interquartile range, is defined as
. This is a standard way to exclude outliers. Initially, the median probability is 2.8% with no movement (1 country). With two more movements (3 countries), the median probability already doubles for both datasets. The probability can reach over 25% for the clients who have visited 14 countries or more, which is nearly 9 times more than the baseline (1 country, no movement). The median probability decreases slightly after 20 country movements in the Gowalla dataset (23 in Foursquare dataset). This is due to the small sample of users with high numbers of movements in the datasets, causing higher variance. For instance, there is only one Gowalla user that has visited 20 countries, and thus the last data point reflects the probability of only that user. Overall, the probability of an adversarial AS compromising a client-guard connection significantly increases during country-level movements.
Protocol. Counter-RAPTOR  improves Tor’s security against BGP hijacks  by changing the way that guards are chosen. For each guard , a client calculates a resilience value that estimates the fraction of Internet ASes that wouldn’t succeed in hijacking the client’s traffic to by (falsely) claiming to be the origin AS of the IP prefix containing . The guard relay selection algorithm combines the resilience value and the bandwidth of the guard by a configurable parameter in order to take into account both the resilience to hijack attacks and load balancing.
Attack. We consider an adversary controlling a single AS whose goal is to observe the sensitive client-guard traffic (as in Section 5.2) via a BGP hijack of the guard’s prefix. Counter-RAPTOR aims to maximize Tor clients’ resiliencies to hijack attacks by choosing a guard relay based on client location. However, the guard selection is done based on the client’s initial location, and the same guard is used for several months even though the clients may move across locations. Thus, AS-level adversaries have an increased power for succeeding in a hijack attack when clients move to new locations because the guard is only optimized for the initial location.
Methodology. We evaluate the CAIDA top 50 ASes  as potential adversaries. We measure the probability of these ASes to succeed in at least one hijack attack during the client’s movements. Note that a successful hijack attack allows the adversary to observe the traffic between client and guard, enabling the traffic analysis attacks discussed in Section 5.2. For each client and each guard, we use AS path inference to determine if an adversary AS can succeed in a hijack attack on the guard. We then compute the attack probability by weighting across the bandwidth of the guards.
Figure 2 shows the probability of succeeding at least one hijack attack on the client’s guard, averaged over the top 50 ASes. Each point on the line shows the median value over clients with a given number of country-level movements. The shaded area shows values in the range . We can see that the median probability of hijack success reaches about 68% with only one movement (2 countries) for both datasets, compared to an initial of 58% and 50%, respectively, with no movement (1 country). When the number of movements is more than 10 countries, the attack probability can reach over 90%. The median probability decreases slightly when the number of countries reaches more than 12 in the Gowalla dataset due to the same reason as in Figure 1; when the number of movements increases, there are fewer users resulting in higher variance. Overall, our analysis shows that the probability of hijack success can quickly increase with only very few client movements.
Protocol. HORNET  (short for High-speed Onion Routing at the NETwork layer) provides similar privacy guarantees as onion routing but operates at the network layer. HORNET builds on routing protocols in which the source can obtain potential routing paths to the destination. Each router cryptographically modifies the full packet (i.e. headers and payload) such that each AS on-path between a source and destination can identify only its previous and next AS hops. HORNET’s stated threat model is an adversary who compromises a fraction of ASes, possibly including the destination AS.
Attack. Our attack on HORNET exploits information leakage via auxiliary information about client movements patterns. The threat model for this attack is an adversary who compromises the destination AS. The adversary wants to deanonymize clients who use a service hosted within the destination AS, e.g. an AS who provides cloud services wants to find the true identities of pseudonymous accounts on a video streaming website hosted in its cloud. The clients’ identities are protected by HORNET when logging into their pseudonymous accounts on the destination website. At the same time, the clients may reveal their identities and locations via auxiliary channels. For instance, researchers, activists, and politicians who frequently travel to different countries and give public speeches expose both their identities and locations. Users may also check in using location-aware services such as Foursquare and Yelp that publicly reveal that information. Identified user movements may also be collected by a cellular provider and shared with the website for commercial purposes or with a common legal authority. Note that the auxiliary location information is not directly linkable to the users’ accounts on the destination website.
The adversary AS has access to two sets of data: (1) pseudonymous accounts of its clients and the preceding hop in the AS path from the clients to the destination AS; (2) auxiliary information that contain real identities of people and their location data.
The adversary’s goal is to link the two datasets and connect the real identities to the pseudonymous activities of the clients. The intuition is that the penultimate hop used to reach the destination AS depends on the location of the client. With dataset (1), the adversary can identify a set of possible client locations for each connection to a pseudonymous account by considering which locations could choose a path to the destination through the observed penultimate AS hop. Then, with dataset (2), the adversary can examine the location data of identified users and ask the question: which identities were in one of the possible locations for the connection? This anonymity set could be quite large if only one pseudonymous connection is considered, but by linking many connections over time the adversary can derive new information when a client moves to new location and thus shrink the client’s anonymity set.
Methodology. As described in Section 5.1, we use the Foursquare and Gowalla datasets as auxiliary information that reveal real client identities and their locations. We map each client geo-location to the AS of the top Internet Service Provider that offers service in the country of the geo-location. Before any location information, each client’s anonymity set comprises the entire population of Gowalla users and Foursquare users, respectively. We then consider location data points at a daily granularity. We assume the clients have daily connections to the destination. For each day, we take all the clients with location data and compute the penultimate AS hops given their mapped source ASes. Then, for each client, we can eliminate all the other clients without the same penultimate AS hop from its anonymity set. Given the imperfect auxiliary information (not all clients have location data everyday), we only eliminate clients with location data the same day, and assume that the clients who do not have data can be at any arbitrary location and thus do not eliminate them from any anonymity set.
After processing all location data from the dataset, we rank the remaining candidates in each client’s anonymity set based on their total number of location data points, from highest to lowest. The intuition is that we want to place more confidence in the clients who reveal their locations frequently and thus provide more identifying information. We also assign a weight value to each candidate as , where and is the number of location data points of candidate . The value of was chosen based on the distribution of number of location data points to scale down the numerical values. Then, for each candidate, we compute the weight ratio of its weight over the sum of all candidates’ weights in the given client’s anonymity set.
Next, we focus only on the highest-weight candidate in each anonymity set. If the weight ratio of the highest-ranked candidate is above a threshold, then we will guess this candidate as the source; otherwise, we “reject” the input (i.e. do not guess) due to a lack of confidence. Note that we allow a single client to have multiple pseudonymous accounts on the destination website, as we may guess the same candidate for multiple client accounts in the above process.
We evaluate the accuracy rates vs. the number of location data points with fixed rejection rate  to show the effectiveness of the attack. Accuracy rate is computed as the number of correct guesses over the total number of guesses, and rejection rate is computed as the number of non-guesses over the total number of pseudonymous accounts.
Results. We present the results for destination Fastly (AS541130), which is a CDN provider for reddit.com and many other websites. We also evaluate other popular sites measured by Alexa  such as Google (AS15169), Facebook (AS32934), and Twitter (AS13414), the results for which show similar patterns and appear in Appendix A.2. We evaluate clients for which we have sufficient location information, which, in this case, are the clients with at least 100 location data points in the datasets (there are such clients in Foursquare and in Gowalla). Figure 3 illustrates the results for fixed rejection rates of 80% and 90%, respectively, for both datasets. For each data point, we bucket clients using increments of 50 location data points. For clients with more than location data points, we group them all together since there are many fewer clients. We can see that the accuracy rate quickly increases with the number of location data points. For Gowalla users, with 200 location data points or more, the accuracy rate reaches 100% with rejection rate at 90%, meaning that the adversary can deanonymize 10% of the clients with no false positives. With lower rejection rate at 80%, the accuracy rate eventually reaches 100% as well for clients with 300 location data points or more. For Foursquare users, the accuracy rate reaches 97% at 90% rejection rate with 300 location data points or more, and reaches 100% with 350 location data points or more for both rejection rates. The difference between the two datasets can be due to the frequency of client movements, e.g., a client with 300 data points of the same location may not reveal that much information compared to a client with 300 data points spread across 10 different countries. From Table 3, we can see that Foursquare users travel less frequently than Gowalla users.
We show that client mobility can expose new vulnerabilities that put mobile clients at risk. For Vanilla Tor, client mobility increases the probability that an AS-level adversary can observe the traffic between clients and guards; for Counter-RAPTOR, client mobility increases the probability of succeeding in routing attacks; for HORNET, client mobility makes it very effective and accurate to deanonymize clients that reveal a sufficient amount of location information.
6 User Behavior
In some of the anonymity systems that we study, clients leak partial information about their network location through observable parts of their anonymity paths. If an adversary learns such information from a single connection, then an adversary that can link multiple connections as originating from the same client may learn increasing amounts of information. In this section, we attack two protocols, DeNASA and Dovetail, with an adversary who links together observations over time. Additionally, we refer an interested reader to Appendices B.1 and B.2, where we provide supplemental results exploring how our Tempest attacks can be extended to two similar systems: Counter-RAPTOR and PHI.
Protocol. DeNASA  is a proposal to improve Tor’s security by modifying how relays are selected for circuits. It is designed to prevent deanonymization via traffic correlation by a small number of Suspect ASes, that is, ASes that appear frequently on paths to or from the Tor network. In the DeNASA guard-selection algorithm g-select, clients make a bandwidth-weighted choice of guard only from among relays that are suspect-free. A suspect-free relay is defined as a relay such that neither of the top two Suspect ASes appear on the network path between the client and the relay. Suspect ASes are globally ranked in descending order of how often they are in a position to perform traffic correlation on Tor circuits.
Attack. The subset of suspect-free relays available to a client varies depending on the client ISP’s Internet links and routing policies. Because of this variation across client locations, clients in different locations will often select the same guard with differing probabilities. Thus, a guard selection leaks some location information; an adversary who can identify a client’s guard can attempt to infer the client’s location by considering guard selection likelihoods from various locations. In this Tempest attack, we demonstrate that multiple guard selections, identified by the adversary over time as belonging to the same client, can leak enough information to reveal the client’s AS. Recall that identifying the client’s AS is a serious degradation of anonymity (Section 4).
To run this attack, the adversary runs some Tor relays. The malicious relays will occasionally be used for a client’s circuits, allowing the adversary to discover the client’s guards over time . The adversary may also employ other known guard discovery attacks [22, 45]. We suppose that the adversary is able to link together a client’s connections over time using the methods described in Section 4, which can all be accomplished by an adversary running relays. Having identified guards
used by the same client over time, the adversary performs Bayesian inference to compute the posterior probabilityfor each possible client location . The adversary uses whatever prior belief he has about the client location (we use a uniform prior). Clients use public routing data to identify suspect-free relays, and so the adversary can compute the guard-selection distributions for all client locations needed to compute the posterior distribution over locations. Increasing numbers of identified guards reduces the uncertainty in this posterior distribution and can effectively reveal the client location.
Methodology. We analyze this information leakage over multiple guard selections by simulating DeNASA’s g-select algorithm using our network model (Section 4). We identify a maximal connected component containing ASes in the graph generated from our AS path inference to use as Tor client locations; we restrict our analysis to a clique to prevent inaccuracies arising from missing edges in the inferred graph. For each of these client ASes, we compute the suspect-free guard selection distribution that clients inside these ASes will use to choose guards. Following Barton et al. , we use AS1299 (Telia Company) and AS3356 (Level 3) as the suspect ASes that clients try to avoid.
Since the adversary performs Bayesian inference in this attack, we use the entropy of the adversary’s posterior distribution over client locations as our measure of client anonymity. We employ a number of heuristics to search for worst-case “leaky” client ASes from which guard selections reveal location information quickly; for example, one heuristic ranks client ASes in ascending order by the mean entropy of the adversary’s posterior distribution after a single guard selection is made (averaged over guards). We select the top ten leaky client ASes identified by our heuristics to evaluate.
For each of these ten client ASes, we simulate a client inside the AS making guard selections (with replacement). We compute the likelihood that these selections were made by a client in each of the possible client ASes. From these likelihoods, we generate the adversary’s posterior distribution over all possible client locations using a uniform prior. We perform this simulation 100 times for each of the ten client ASes.
Results. In Figure 4
we show the entropy of this posterior location probability distribution, averaged across the samples, for each client AS at varying number of guard observations. For visual clarity, we only present four of the evaluated ASes (the other six ASes exhibit similar or identical trends). After two observed guard selections, clients from all ten ASes drop below 4 bits of entropy. After three observations, the median entropy for these ASes drops to just 1 bit.Observe how multiple observations combine to cause dramatic reductions in anonymity. For example, after a single guard selection observation, clients in AS199348 only lose an average of 1.34 bits of entropy; however, a second observation, in combination with the first, yields a substantial loss of 13.26 bits and depletes nearly all of the client’s entropy. A client is forced to select two or more guards quickly (e.g. in hours or days) if his guards go offline and is guaranteed to select a new guard every few months.
Protocol. Dovetail  is a network-layer anonymity protocol designed for networks that support source-controlled routing. In Dovetail, a source host builds a route to a destination host using overlapping head and tail path segments to and from a matchmaker AS , which is chosen randomly from a set of available matchmaker ASes. builds the head path segment to and encrypts ’s identity to . and coordinate to choose and build the tail segment to such that head and tail path segments intersect at a common AS, , called the dovetail. After removes the loop to the matchmaker, the final path used for communication is . ASes on a Dovetail path learn: (1) the identities of their immediate predecessor and successor ASes on the path, (2) their absolute position in a path (e.g. first hop, second hop, etc.), and (3) the host/AS at the end of their path segment (ASes on the head segment learn ’s identity, ASes on the tail segment learn ’s identity, and learns both and ’s identity).
In source-controlled routing, a client chooses the AS path used for his connections from a subset of paths made available by his ISP — we call this path subset routable paths. To build a Dovetail connection, a client randomly chooses a head path segment from among his routable paths containing six or more ASes to the matchmaker; the dovetail is chosen to be the second-to-last AS on the head path segment. The details of tail path selection are unimportant for this attack.
Attack. To reiterate, a client is limited to select a head path segment to the matchmaker from among routable paths; the availability of routable paths varies depending upon the client’s ISP, and so path usage inherently leaks location information. If the adversary compromises the th AS on a path, then he can narrow down the client’s location by considering the set of ASes that could have created a path of length to the predecessor AS in the path.
With our Tempest attack, we demonstrate that this leakage is greatly exacerbated if a single Dovetail client makes many connections. Suppose the adversary controls an AS that monitors connections passing through his AS, a scenario that is within Dovetail’s stated threat model. We require that the adversary links together connections as originating from the same client (e.g. using a method described in Section 4). The adversary will use his path observations when he happens to be chosen in the dovetail position because this position is the closest to the source that also learns the connection’s destination, which facilitates the connection-linking required for this attack. Suppose the adversary makes observations with respect to a client, where denotes the adversary’s predecessor and absolute position on the th connection on which he is in the dovetail position; then, the adversary can compute the set of possible client locations as where maps a predecessor and position to the set of client locations that can create a path of length to . The output of this attack is a possibility set over client locations, in contrast to the probability distribution over client locations obtained by the adversary in the Section 6.1 attack; computing the observation likelihoods required for Bayesian inference is computationally expensive given Dovetail’s route selection scheme.
Methodology. We run our attack in a simulated Dovetail network with paths inferred from CAIDA’s Internet topology. We follow Sankey and Wright and use a network model in which a client can route a connection through any valley-free AS path between the source and destination with at most one peer-to-peer link. ASes without customer ASes act as possible client ISPs in this analysis; there are such ASes in the topology.
Dovetail Frequency. We assume the adversary compromises a single, fixed AS; as such, interesting ASes to consider as compromised are ones that are selected often as a dovetail. We run simulations to determine ASes that are likely to be selected to serve as the dovetail. In each simulation, a source AS and matchmaker AS are chosen uniformly at random from among all client ISP ASes and all ASes, respectively. Then, we simulate a client in the source AS who builds a path to the matchmaker AS and record the dovetail AS. We collect samples by repeating this procedure times and choose as our adversarial AS the most-common dovetail.
Anonymity Evaluation. We run simulations to measure the efficacy of this attack with respect to the fixed adversarial AS. In each simulation, we choose a source AS uniformly at random from among all client ISP ASes and choose 500 ASes uniformly at random from among all ASes to serve as matchmaker ASes. Then, we simulate a client in the source AS who makes up to 100 repeated connections to the same destination (the destination location is irrelevant for this analysis). For each connection, the client chooses a matchmaker AS uniformly at random from among the 500 possible and builds a path to the matchmaker. If the fixed adversarial AS happens to be placed in the dovetail position on this path, the adversary observes his predecessor AS in the path and his ordinal position in the path. The adversary then computes the set of all ASes who could have constructed a path of observed length to his predecessor and, through intersection, updates his set of possible client locations maintained persistently for the entire simulation (initially containing all client ISP ASes). If the adversary is not selected as the dovetail for a connection, he simply takes no action. We record the size of the possible client location set after each of the 100 source-matchmaker paths are constructed. We collect samples by repeating this procedure times.
Results. Dovetail Frequency.
AS1299 (Telia Company) is selected most frequently as the dovetail AS, used in 4.03% of all samples. The distribution is very right-skewed — althoughunique ASes are selected as dovetail at least once, the top ten ASes of the distribution are used as the dovetail in 30.73% of all samples.
Anonymity Evaluation. Because AS1299 was selected as the dovetail most frequently in our frequency analysis, we use it as our fixed adversarial AS. We also ran experiments for the other nine most-frequently-selected dovetail ASes and find that they all produce similar results. Figure 5 plots the number of ASes in the source’s anonymity set after repeated connections. Each line corresponds to a percentile; for example, the point at in the “5th” percentile indicates that, after 100 connections, AS1299 could rule out all but 126 possible source ASes in 5% of samples. This attack yields significant anonymity reductions in many of our samples. After 10 repeated connections, in 5% of samples, our adversary can rule out all but source ASes as possible — a 90% reduction in anonymity set size. By 100 repeated connections, the 5th percentile across samples is reduced by 99.7%.
These results exhibit a continual reduction in anonymity as the client make connections, suggesting that clients who make many connections place themselves at a high risk for deanonymization. A client could make hundreds of repeated connections over a few days/weeks if he is a frequent user of some Internet service. We find that each of the ten most frequently selected dovetail ASes are strong vantage points for this attack. Many of these ASes are Tier 1 or large transit networks; such ASes are naturally of interest to many adversaries and are high value targets for compromise.
For both the DeNASA and Dovetail protocols, we demonstrate serious weaknesses to an adversary who can link observations together. We emphasize that the significant anonymity degradation in our findings occurs only after a client makes multiple connections, whereas prior work has focused on quantifying anonymity after a single observed connection.
7 Routing Changes
Natural Internet routing dynamics can change the network paths between hosts in an anonymity system. In this section, we show how an adversary can use such route changes to deanonymize users of two systems: TAPS and HORNET.
Protocol. Trust-Aware Path Selection (TAPS)  proposes to improve Tor’s resistance to traffic-correlation attacks by choosing circuits based on the trust that a client has in different network components. TAPS models a trust belief as per-adversary probability distributions that describe the likelihood that relays and network paths between relays are under observation by each adversary. Because different client locations use different network paths, TAPS leaks information about the client’s location through how the client chooses its circuits’ relays. To limit this leakage, TAPS clusters client locations around a fixed set of representative locations, and then each client chooses circuits as if it were in its cluster’s representative location. The clustering routine is informed by a static snapshot of the state of the network at the time of cluster generation. This snapshot contains information about the entities in the trust belief, such as the Tor relays and the ASes. Importantly, it includes inferred AS-level routing paths. Because the state of the network changes over time, Johnson et al. state that clusters should be reformed periodically.
Attack. There exists a significant temporal issue with the TAPS clustering process: a client’s circuit-construction behavior may reveal his cluster, a client’s cluster may change across cluster reformations, and the adversary can intersect those clusters to gradually reveal the client’s location. The TAPS AS-to-cluster assignments are public, and so the adversary can use them to perform this deanonymization.
For our attack, we suppose the adversary employs a variant of the Chosen Destination Attack  in which the client repeatedly connects to a malicious website over a longer timescale. The adversary runs a number of web servers hosted in different ASes and includes resources from each server in the website. Each time the client connects to the site, the adversary uses his servers, in conjunction with a guard discovery attack [22, 45], to observe the client’s circuit-construction behavior. As demonstrated against Astoria, these observations can identify the client’s representative location and thus his cluster. After the adversary determines a client’s clusters across cluster formations, then the adversary knows the client’s AS is in the set . As cluster composition varies across reformations, the client’s AS can eventually be revealed (Section 4 describes how revealing the AS degrades the client’s anonymity).
Methodology. We measure this degradation by running the TAPS clustering routine on archived Tor and Internet routing data. We implement TAPS (specifically, the TrustAll configuration with The Man trust policy) to conservatively evaluate how it is affected by network changes. In particular, we omit AS organizations, IXPs, and IXP organizations as possible sources of compromise, and we use a fixed prefix-to-AS mapping at all times. These choices should make our anonymity analysis conservative, as anonymity degrades faster the more that the composition of TAPS clusters changes across cluster reformations. By implementing TAPS to use fewer data sources and more static data, we expect that the clusters generated by TAPS vary less in composition across reformations.
We perform twelve TAPS cluster formations — one for every month in 2016. We use CAIDA’s serial-2 AS relationships and CollecTor’s Tor data for each respective month, and a fixed Route Views prefix-to-AS mapping set from Jan 2016 for each clustering. Following Johnson et al. , we use the top Tor client ASes identified by Juen  as medoid centers and configure TAPS to generate 200 client clusters. We verify that our TAPS implementation is deterministic on input (i.e. changes in AS-cluster assignments are driven by changes in network data, and not by some use of randomness within the clustering algorithm).
We identify a set of stable ASes, that is, those that exist in all twelve clusterings (ASes are only clustered if they exist in the CAIDA relationship data set for that month). We measure the anonymity-set size of each of these stable ASes after a variable number of consecutive cluster formations; namely, where maps an AS to the ASes in its cluster in month .
Results. Figure 6 plots distributions of anonymity-set sizes. Each CDF corresponds to the distribution after consecutive cluster formations. The CDF shows the anonymity-set-size distribution after the initial clustering in Jan, shows the distribution after cluster formations in Jan and Feb, etc. Each point on a CDF corresponds to the anonymity-set size for one of the client ASes.
TAPS cluster reformations have serious consequences with respect to user anonymity. Initially (), the TAPS clustering assignment does well at producing large clusters with similar sizes: the 5th, 50th, and 95th percentiles are all of the same order of magnitude with sizes of 103, 445, and 927 ASes, respectively. However, just a single reformation can harm large fractions of users. After just a single reformation in Feb (), we observe a 94% reduction in anonymity-set size, from 445 to 28 ASes, for the median client AS, and a 99% reduction in size from 154 to 2 ASes at the 10th percentile. By six total clusterings, (), 49% of ASes are left with singleton anonymity sets. Clients from any one of these ASes in this near-majority are rendered identifiable to their AS by their circuit-construction patterns over time. By being patient and exploiting this temporal vulnerability in TAPS, the adversary can learn many clients’ locations.
Protocol. Refer to Section 5.4 for a more complete description of the HORNET protocol. The important properties of the protocol to recall are: (1) HORNET provides anonymity at the network layer; (2) the identities of the source and destination hosts of a connection are hidden from all ASes carrying the connection’s traffic except the source and destination ASes, respectively; and (3) ASes learn their immediate predecessor and successor ASes on a connection’s path.
Attack. For this Tempest attack, we observe that network routes can change over time and that such changes can leak information about the client location. Consider a compromised AS that observes a connection to a destination server within . The adversary observes the penultimate AS hop of this connection. Thus, the adversary knows that the client’s location is in the set containing all ASes that currently have a route to with penultimate hop . Now, suppose a route change occurs that causes the client to reconnect through penultimate AS hop . We assume that the adversary can link the reconnection to the prior connection via traffic analysis (e.g. the destination typically has only one active connection or a higher-level protocol has an identifiable handshake pattern at connection start). The adversary can then compute the set containing ASes that route to through penultimate hop and conclude that the client’s AS is within . Thus, as with the previous attack (Section 7.1), the client’s anonymity is degraded by learning its AS.
To run this attack, the adversary (1) controls an AS , (2) monitors connections to a third-party destination host within , and (3) waits for route changes to occur that affect the connections he is monitoring. The adversary must also have some source of data about the available routes to , several of which are currently available, including public routing datasets  and public platforms for traceroute measurements . We assess the risk this attack poses by quantifying the frequency of naturally-occurring route changes and the impact that a single change can have on anonymity.
Methodology. We use traceroute data made available by RIPE Atlas  to analyze Internet routing changes. RIPE Atlas is an Internet measurement platform consisting of thousands of volunteer-run network probes. These probes are distributed across thousands of different networks and can be configured to run various Internet measurements, such as pings and traceroutes.
We consider one such Internet measurement (id ); in this measurement, all online probes run UDP traceroutes to k.root-servers.net. In Jan–Feb 2016, the period we consider for this study, this name resolved to 126.96.36.199 in AS25152. There were approximately probes hosted across IP prefixes, representative of 10–11% of allocatable IPv4 space, running traceroutes to this destination every 30 minutes. For the sake of this analysis, we consider the scenario where AS25152 is under adversarial control and contains a destination host of interest to the adversary.
We identify a set of stable probes whose IP prefix and AS of origin do not change during our measurement period; we want to ensure that route changes that we observe are due to changes in network routes and not “artificially” induced by a probe’s physical location changing. We identify stable probes hosted across unique ASes. These probes will serve as the clients our adversary will attempt to deanonymize.
Frequency of Changes. We use Route Views prefix-to-AS mappings to compute the AS path each probe uses to reach AS25152. At the granularity of the measurement interval (30 minutes), we search for route changes that cause a change in a stable probe’s penultimate hop to AS25152 between two traceroutes. We perform this search for all stable probes over the entire month-long period. We compute the mean number of penultimate hop changes each AS experiences from Jan–Feb (averaging over probes).
Impact of Changes. For each route change we identify in a stable probe ’s traceroute data, we compute two sets: , containing all probes with traceroutes matching ’s penultimate hop before the route change occurred, and , containing all probes with traceroutes ’s penultimate hop after the route change occurred. We then measure ’s anonymity set size with respect to and (i.e. before and after the route change) by computing and , where maps a probe to its Autonomous System of origin. Following this method, we compute the mean before-and-after anonymity set sizes for each AS with at least one route change by averaging over all route changes and stable probes.
Results. Frequency of Changes. We find that 840 (30%) of the ASes experience at least one penultimate hop route change from Jan–Feb and that the distribution of changes is long-tailed. 20% of ASes experience at least 2 penultimate hop changes on average, 10% of ASes experience at least 3.8 changes, 5% of ASes experience at least 7.9 changes, and 3% of ASes experiences at least 50.8 changes. ASes that had at least one route change are susceptible to this attack. Frequent route changes can further expose users both by increasing the probability that an adversary can make at least one route change observation for a user and by introducing the possibility that multiple route changes can be linked to a single user. Our results are limited to locations with probes, but, due to the large sample-size of probes, the distribution of frequencies we obtain should apply to the Internet as a whole.
Impact of Changes. Figure 7 depicts the distribution of mean anonymity set sizes for each of the 840 ASes with at least one change before and after a single, average routing change occurs. A nontrivial percentage of these ASes (8%) are left with singleton anonymity sets after the average route change. There is a sizable reduction in anonymity for most ASes; for example, the median shifts from an anonymity set size of 170 ASes to just 8 ASes. The anonymity sets formed in this analysis are limited to locations with probe coverage. This particularly affects the absolute anonymity-set sizes. However, we expect that the relative reductions in anonymity (e.g. an order of magnitude) reflect an adversary’s ability in practice.
These results present a serious risk for users. Suppose that the destination under surveillance in AS25152 serves live client connections on average at any given time and suppose that users are distributed roughly uniformly across the Internet AS space. From our analysis above, we would expect that connections will experience a penultimate hop route over the month (see Frequency results, we found that 30% of source ASes experienced a penult. hop change). If the destination is popular or connections are long-lived, will be large and thus many users will be vulnerable to this attack.
For both TAPS and HORNET, we show that a patient adversary who waits for network changes (cluster reformations in TAPS, route changes in HORNET) can use public datasets (cluster assignments, traceroute data) to achieve order-of-magnitude reductions in clients’ anonymity set sizes. As the network continues to change, clients ASes can be completely identified.
8 Discussion and Ethics
Countermeasure Challenges. Explicitly accounting for temporal dynamics could reduce the severity of the Tempest attacks. However, the most straightforward defenses encounter subtle tradeoffs and weaknesses. For example, an obvious approach to defending against the client-mobility attacks on vanilla Tor and Counter-RAPTOR is for the client to select multiple guards in different locations and use the one closest to current location. However, this raises an immediate tradeoff between defending against Tempest and limiting the number of potentially malicious relays in the guard position. This approach also raises the possibility of an attack in which an adversary places guards in targeted locations to affect nearby clients. As another example, a natural attempt to prevent the user-behavior attack on Dovetail would be for each client to choose a small number of matchmakers to use for all connections. However, as noted by Sankey and Wright (Section 5.1 ), reusing the same matchmaker gives it the ability to perform an intersection attack across connections based on their tail segments, as the client chooses each tail segment to minimize intersections with the head path. As a final example, a plausible defense against the routing-change attack on HORNET might seem to be for the client to choose paths for which the penultimate hop changes infrequently. However, in addition to making a client’s anonymity dependent on routing dynamics outside of its control, this creates an additional information leak to the adversary, who could take into account path variability when considering which client location is a likely source for an observed connection. Thus defending against the Tempest attacks appears to be a non-trivial challenge that we leave for future work.
Active Attacks. In this paper, we mainly consider adversaries passively observing network traffic. However, we do consider an adversary performing active BGP hijacks against Counter-RAPTOR in Section 5.3 and Appendix B.1, and we do include active methods among those that might be used to link connections and perform guard discovery. We leave for future work a more general analysis of the power of active adversaries to exploit temporal dynamics. Such adversaries could be very powerful. For instance, an active adversary may be able to track the movements of a Vanilla Tor client by continually intercepting traffic to the guard. As another example, an active adversary may cause observable routing changes by withdrawing and inserting (possibly completely legitimate) routes in HORNET.
Ethical Considerations. All the datasets we used in this paper were publicly available. With the privacy and safety of Tor users in mind, we refrain from collecting any user data on the live Tor network. Instead, we work with existing public datasets, such as network data from CAIDA and RIPE Atlas, Tor data from CollecTor, and location data from Gowalla and Foursquare, to perform attack analysis while preserving the anonymity of real Tor users. The code used for this paper is available at https://github.com/rwails/tempest for review.
9 Related Work
Temporal Dynamics. Similar to our work, there is a thread of research that deals with the degradation of anonymity over a period of time. In the predecessor attack [53, 65], an attacker tracks users’ communications over multiple path reformulations and identifies the observed previous hops as the most likely sources of the connection. Øverlier and Syverson  used similar observations for demonstrating practical attacks against hidden services, and motivated the use of and research on guard relays in anonymity networks [21, 27]. Intersection attacks [52, 5] and disclosure attacks [41, 16, 44, 17] aim to compromise client anonymity by intersecting over time the sets of clients that were active when a given client is observed to receive a message. Danezis and Troncoso highlighted the impact of evolution of user behavior in disclosure attacks .
In contrast to previous works, we identify and analyze novel traffic analysis attacks based on exploiting temporal changes in anonymity paths, specifically in the context of low-latency anonymity systems and AS-level adversaries. We note that our results on client mobility represent the first analysis of this issue in anonymity systems. Furthermore, our results on exploiting user behavior and routing updates represent the first analysis of how probabilistic information leaks due to restricted AS-level Internet topology can be aggregated over time. Our work is also unique in systematically investigating these issues across a broad range of systems.
Network-Level Adversaries. Security analyses of anonymity systems typically focus on the threat of end-to-end timing correlation by compromised or malicious relays/proxies . Feamster and Dingledine were the first to consider this threat from the perspective of network-level adversaries . Murdoch and Zieliński  showed that even Internet-exchange level adversaries can perform traffic analysis of anonymity systems. Edman and Syverson  measured the impact of Tor’s path selection strategies on security against network-level adversaries. Johnson et al.  performed a security analysis of Tor and measured the risk of deanonymization against both relay-level and network-level adversaries over time. Sun et al.  were the first to observe that an adversary could manipulate routing dynamics (BGP) to compromise user anonymity in Tor, including exploiting inherent churn in BGP. These works have motivated the design of several of the systems we study in this paper, and our work shows that that the deanonymization risk in those systems is much greater than previously thought.
Latency Attacks. Hopper et al.  demonstrated that a malicious destination can infer a client’s location after a number of repeated connections using information leaked via connection latency in Tor. Latency attacks are orthogonal to Tempest attacks and can be used in parallel to enhance the adversary’s ability to deanonymize users. However, Tempest attacks are applicable in cases where latency attacks may be ineffective, e.g. in source-controlled routing networks where the availability of many routable Internet paths may limit information leaks from latency.
Other Traffic Analysis Attacks. Our work highlights the risk of abstracting away important system components that impact user anonymity in practice. Similarly, prior work on traffic analysis has considered a range of related oversights. Mittal et al.  analyzed the impact of network throughput information, and showed that it allows an adversary to infer the identities of Tor relays in a circuit. Murdoch and Danezis  and Evans et al.  considered the impact of network congestion on anonymity systems such as Tor. Borisov et al.  and Jansen et al.  explored the use of denial of service attacks to compromise client anonymity. Murdoch  and Zander et al.  have shown that clock skew can be used for deanonymization.
We identify temporal dynamics in anonymity paths as potentially degrading the security of anonymous-communication systems. We present the Tempest attacks, which make novel use of such dynamics in three broad categories, including path changes due to client mobility, user behavior, and network routing updates. These attacks are shown to be effective against a variety of anonymity systems including both onion-routing and network-layer protocols. Our work leads to the following recommendations for the research community:
Adversarial Model: Anonymity systems should consider the threat of a patient adversary that is interested in performing long-term attacks on anonymity systems. Such an adversary can record information about user communications over a long period of time, and then (1) aggregate probabilistic information leaks over time to deanonymize users, and (2) correlate information leaks with auxiliary sources of information, such as data about client mobility patterns or network routing updates, to deanonymize users.
Temporal Dynamics: Anonymity systems should be analyzed for use over time. In particular, system designers ought to consider the effects client mobility, user behavior, and network routing changes. More generally, our work motivates (1) the design of anonymity protocols that are robust in the presence of temporal dynamics, and (2) the formalization of security definitions and frameworks that incorporate relevant temporal issues.
This work was supported by the Office of Naval Research (ONR) and also by the National Science Foundation (NSF) under under grant numbers CNS-1423139, CNS-1527401 and CNS-1704105. The views expressed in this work are strictly those of the authors and do not necessarily reflect the official policy or position of ONR or NSF.
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Appendix A Client Mobility
Protocol. Refer to Section 6.1’s protocol description; briefly restated, DeNASA clients who use g-select choose guards only from among relays that are suspect-free; i.e. clients ensure two suspect ASes do not exist on their client-guard network paths.
Attack. In the similar manner as the attack we outline in Section 5.2, we consider the risk of traffic-correlation by one of the suspect ASes as clients move between network locations. Although the client initially chooses a suspect-free guard, the client may introduce one of the suspect ASes onto his client-guard network path as he moves to new locations while continuing to use the same guard.
Methodology. We quantify the increasing probability that the suspect ASes will be able to observe client-guard connections during clients’ movements. We simulate mobile clients following our mobility model in Section 4. Using guard weights, we compute the probability that a client will choose a guard such that the client-guard link is compromised by a suspect AS at least once over the client’s movements. We compute this compromise probability for each client in both the Foursquare and Gowalla dataset.
Result. Figure 8 shows the distribution of clients’ probability of compromise, where clients are grouped together by their number of country-level movements. Each point on the line shows the median probability of compromise over clients with a given number of country-level movements. The shaded area shows values between , where and are Quartile 1 and Quartile 3, respectively, and IQR, the interquartile range, is defined as . Initially, all clients are able to find suspect-free guards, and so clients with no movements have no probability of being compromised. Once the clients start moving to different countries, the probability increases. There is large variance in probabilities among clients when the number of country-level movements is relatively small (8 or fewer countries for Gowalla users and 10 or fewer countries for Foursquare users) — for some clients, the probability remains close to 0, while for others it can get close to 1 after visiting only six countries in both datasets. The rate of increase in probability depends on the countries that a client visits and the order of the visits.
a.2 HORNET Supplemental
In Section 5.4, we present the accuracy rates for destination Fastly (AS54113) with 80% and 90% rejection rates. We have also performed the same evaluations with respect to Google (AS15169), Facebook (AS32934) and Twitter (AS13414). We show the results (with 90% rejection rate) in Figure 9 based on the Gowalla dataset.
We can see that the overall trends are similar for all four ASes — the accuracy rate quickly increases with the number of location data points. For both Facebook and Twitter, the accuracy rates reach 100% when there are at least 225 location data points. For Google, the accuracy rate reaches 100% when number of location data points reaches 250, however, it goes back down to 87.5% when considering only clients with 300 location data points or more. This could be due to the limited number of clients in the 300-data-point group which leads to lower accuracy rate.
Appendix B User Behavior
Protocol. Recall from Section 5.3 that Counter-RAPTOR is another proposed client-location-aware modification to Tor’s guard selection. In Counter-RAPTOR, clients incorporate a BGP hijack resilience value into guard selection probabilities.
Attack. Guard resiliency varies across client locations; a guard may be resilient to hijacks with respect to some client locations but not others. As such, in the same manner as DeNASA, guard selections that can be linked to a client by the adversary can be used to infer the client’s location. Counter-RAPTOR does offer some defense against this attack; to prevent relay load from becoming too skewed by resilience values and to limit location information leakage, Counter-RAPTOR clients weight guards by a configurable linear blend of resilience and bandwidth weight. We run the same Tempest attack described in Section 6.1 to evaluate the efficacy of the Section 6.1 Tempest attack on Counter-RAPTOR.
Methodology. We implement Counter-RAPTOR using archived Tor and Internet topology data. We adhere to the adversary model and methodology laid out in Section 6.1, with a caveat that we use older Tor data from Oct 2015 for this analysis, but AS topology data derived from CAIDA’s Oct 2016 datasets. We do not expect that this inconsistency in methodology significantly affects our results. We configure Counter-RAPTOR with configuration parameter , i.e., clients weight guards 50% by their resilience value and 50% by their bandwidth. is the default value recommended by Sun et al. . We compute the Counter-RAPTOR guard selection distributions for all clients locations in a fully-connected component of ASes.
We perform a search for leaky client locations among the ASes using the heuristic search techniques described in Section 6.1. In total, we select the top 18 ASes from our search heuristics for evaluation. We perform 100 simulations from each of these 18 locations of a client selecting up to 500 guards. In each simulation, we form a posterior distribution over client locations after each guard selection, assuming a uniform prior.
Results. Figure 10 shows the entropy of this posterior client location probability distribution, averaged across samples, for each client AS at varying number of guard observations. For visual clarity, we only include the five ASes whose samples had the smallest mean entropy after 100 guard selections. Guard selections from the leakiest ASes we evaluated for Counter-RAPTOR are not nearly as informative as we found in DeNASA. After 100 guard selections, clients in AS6185 are the worst off, but only have lost bits of entropy on average. Clients do leak significant amounts of information over hundreds of guard selections, e.g. clients in AS174 have 1.1 bits of entropy on average after 500 guard selections; however, it is very unlikely that a client will need to select this many guards and so the slow leakage may be acceptable for most users. Though our search methods are not exhaustive, these results suggest that Counter-RAPTOR’s bandwidth blending may be a suitable method for limiting location information leaks in location-aware relay selection algorithms.
Protocol. PHI, short for Path-Hidden Lightweight Anonymity Protocol, is a network-layer anonymity protocol that protects users by using encrypted routing state stored in packet headers and by using path-setup traffic indirection in a similar fashion to Dovetail. PHI’s design improves upon two of Dovetail’s shortcomings: (1) PHI uses fixed-size packet headers and randomized placement of routing state within packet headers to prevent ASes from learning their absolute positions on a path, and (2) PHI offers compatibility with BGP networks and does not require an infrastructure that supports source-controlled routing.
In PHI, helper ASes serve an analogous role to Dovetail’s matchmaker ASes. A source host builds a connection to a destination using two half-paths to and from a helper AS which is chosen from a set of available helpers. The process of setting up the first half-path from to reveals as the helper AS to all intermediate ASes on the half path, but ’s identity is hidden from all but the source AS, as the route back to is stored as encrypted segments in the setup-packet headers. uses the half-path to send to the final destination encrypted using the public key of , and then runs a back-off procedure to identify a midway AS who serves a similar role to Dovetail’s dovetail AS. The back-off procedure will choose the midway to be the last AS on the path who can transit traffic from his predecessor AS to without violating any valley-free routing assumptions. builds the second half-path establishing the final end-to-end path . So, ASes on a PHI path learn (1) the identities of their immediate predecessor and successor ASes on the path, (2) their relative position on path (i.e. before the midway, the midway, or after the midway), and (3) the host/AS at the end of their half-path (ASes on learn ’s identity, ASes on on learn ’s identity).
Attack. We run a modification of our Section 6.2 Tempest attack on PHI. In this attack, the adversary compromises a single, fixed AS and attempts to deanonymize a client who is repeatedly connecting to a fixed destination using many helper nodes. Path usage leaks location information in PHI just as it does in Dovetail and so the adversary, when having compromised an AS on a client’s path, can use the topological information he learns to infer the client’s location. When the adversary is on the half-path (and therefore knows ’s identity), we suppose the adversary can link the connection and his observation to the client using one of the linking techniques described in Section 4.
We codify the adversary’s observations, notated as , as triplets containing (1) the adversary’s predecessor AS in the path, (2) the adversary’s relative position on path, and (3) the AS containing . Having made observations the adversary computes posterior probability for each possible client location , assuming a uniform prior. Unlike Dovetail, computing observation likelihoods in PHI is not prohibitive and so we favor a probabilistic approach; however, we conservatively assume the adversary does not know how often non-observations occur, i.e. when the client makes a connection not containing the compromised AS. Because of our conservative approach, the adversary may incorrectly compute some observation likelihoods, leading to an incorrect posterior, and so we cannot accurately use entropy to measure anonymity. Instead, we use the adversary’s posterior probabilities as guessing scores and use accuracy and rejection rates to measure attack efficacy, similar to our Section 5.4 HORNET mobility attack.
Methodology. We run our Tempest attack in a simulated PHI network according to the protocol description above. This simulation is performed on a well-connected AS graph containing ASes. The graph is generated using shortest-path, valley-free inference using CAIDA’s AS relationships.
We note that Chen et al. suggest that PHI clients select helper ASes for a connection by attempting to maximize source-anonymity-set sizes with respect to the connection’s midway node; however, for two reasons, we do not implement this helper selection scheme. First, in that proposal, it is also suggested that clients only use subsets of helper ASes when maximizing anonymity-set sizes to prevent an adversary from learning too much by observing a client’s helper selection, but it is unspecified how clients choose appropriate helper subsets. Second, the method of anonymity-set computation used in PHI does not properly model an adversary who can reason about the actions of clients. Suppose that, in some path, midway AS observes predecessor AS . PHI describes a possibilistic method for anonymity-set computation, in which would include any AS with a valley-free path to in the source’s anonymity set; however, if, for example, all but a few source ASes use with negligible probability, an adversary may be able to determine the source’s AS confidently by way of statistical inference. Correctly maximizing anonymity-set sizes through helper selections in the presence of this adversary (without leaking additional topological information) is a non-trivial task and outside the scope of this work.
Midway Frequency. To choose a compromised AS for this attack, we identify ASes likely to be chosen as the midway in PHI connections. The midway is the closest on-path AS to the source who learns the destination of the connection, and so ASes who frequently serve as the midway are well-positioned to run our linking attack. We draw (source, helper, destination) AS triples uniformly at random from our network and build a connection from the source to the destination via the helper, recording which AS is selected to serve as the midway. We repeat this sampling times.
Anonymity Evaluation. We run simulations to measure how much a single compromised AS can learn in the repeated-connections setting. For each simulation, we (uniformly at random) sample a source AS, a destination AS, and 500 ASes to serve as helper nodes. We simulate multiple PHI connections from the source to destination — for each connection, the source chooses a helper uniformly at random (with replacement) from among the 500 available to him. If the connection passes through the compromised AS such that the compromised AS learns the destination, we compute the (possibly incorrect) likelihoods that this connection was created by a client in each of the possible source ASes. For each observable connection, we use these likelihoods to update the adversary’s belief about the location of the client (maintained persistently for the entire simulation). The adversary starts with a uniform prior belief over all possible source ASes in each simulation. For a single simulation, we consider up to 50 repeated connections from the sampled source to destination. We repeat this entire simulation procedure times.
Results. Midway Frequency. AS174 (Cogent) is most frequently selected, serving as the midway in of all connections; so, we use AS174 as our compromised AS for this attack.
Anonymity Evaluation. Figure 11 plots the deanonymization accuracy the adversary can achieve after the client makes some number of repeated connections at various rejection rates. For example, the point at in the 75% rejection rate line indicates that after the client made 30 repeated connections, the adversary could determine the client’s location in 25% of samples with 35% accuracy (the adversary makes no guess due to lack of confidence in the other 75% of samples).
We find that PHI exhibits weaknesses to our Tempest attack. As a client continues to make connections through the network, the adversary’s ability to correctly determine the client’s location grows significantly. When the adversary is willing to guess for 10% of samples (i.e. at a 90% rejection rate), he only achieves 13% accuracy after a single connection; however, by 30 repeated connections, accuracy increases significantly to 73%. If the adversary wishes to avoid incorrect guesses, he can tune his guessing threshold to 99% rejection and can achieve 100% accuracy after just 2 repeated client connections.