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Differentially Private Obfuscation Mechanisms for Hiding Probability Distributions
We propose a formal model for the privacy of user attributes in terms of...
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Generating Optimal Privacy-Protection Mechanisms via Machine Learning
We consider the problem of obfuscating sensitive information while prese...
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Optimal noise functions for location privacy on continuous regions
Users of location-based services (LBSs) are highly vulnerable to privacy...
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Local Distribution Obfuscation via Probability Coupling
We introduce a general model for the local obfuscation of probability di...
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Discrete Distribution Estimation under Local Privacy
The collection and analysis of user data drives improvements in the app ...
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On the Robustness of Information-Theoretic Privacy Measures and Mechanisms
Consider a data publishing setting for a dataset composed of non-private...
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A Practical Approach to Navigating the Tradeoff Between Privacy and Precise Utility
Due to the recent popularity of online social networks, coupled with peo...
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Local Obfuscation Mechanisms for Hiding Probability Distributions
We introduce a formal model for the information leakage of probability distributions and define a notion called distribution privacy. Roughly, the distribution privacy of a local obfuscation mechanism means that the attacker cannot significantly gain any information on the distribution of the mechanism's input by observing its output. Then we show that existing local mechanisms can hide input distributions in terms of distribution privacy, while deteriorating the utility by adding too much noise. For example, we prove that the Laplace mechanism needs to add a large amount of noise proportionally to the infinite Wasserstein distance between the two distributions we want to make indistinguishable. To improve the tradeoff between distribution privacy and utility, we introduce a local obfuscation mechanism, called a tupling mechanism, that adds random dummy data to the output. Then we apply this mechanism to the protection of user attributes in location based services. By experiments, we demonstrate that the tupling mechanism outperforms popular local mechanisms in terms of attribute obfuscation and service quality.
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