Log In Sign Up

Hide me Behind the Noise: Local Differential Privacy for Indoor Location Privacy

by   Hojjat Navidan, et al.

The advent of numerous indoor location-based services (LBSs) and the widespread use of many types of mobile devices in indoor environments have resulted in generating a massive amount of people's location data. While geo-spatial data contains sensitive information about personal activities, collecting it in its raw form may lead to the leak of personal information relating to the people, violating their privacy. This paper proposes a novel privacy-aware framework for aggregating the indoor location data employing the Local Differential Privacy (LDP) technique, in which the user location data is changed locally in the user's device and is sent to the aggregator afterward. Therefore, the users' locations are kept hidden from a server or any attackers. The practical feasibility of applying the proposed framework is verified by two real-world datasets. The impact of dataset properties, the privacy mechanisms, and the privacy level on our framework are also investigated. The experimental results indicate that the presented framework can protect the location information of users, and the accuracy of the population frequency of different zones in the indoor area is close to that of the original population frequency with no knowledge about the location of people indoors.


page 5

page 9


L-SRR: Local Differential Privacy for Location-Based Services with Staircase Randomized Response

Location-based services (LBS) have been significantly developed and wide...

Geographic Differential Privacy for Mobile Crowd Coverage Maximization

For real-world mobile applications such as location-based advertising an...

Semantically enriched spatial modelling of industrial indoor environments enabling location-based services

This paper presents a concept for a software system called RAIL represen...

Enabling Seamless Device Association with DevLoc using Light Bulb Networks for Indoor IoT Environments

To enable serendipitous interaction for indoor IoT environments, spontan...

Differentially Private Publication of Location Entropy

Location entropy (LE) is a popular metric for measuring the popularity o...

Spatio-temporal Trajectory Dataset Privacy Based on Network Traffic Control

Collection of user's location and trajectory information that contains r...

Practical Location Validation in Participatory Sensing Through Mobile WiFi Hotspots

The reliability of information in participatory sensing (PS) systems lar...