Protecting location privacy: optimal strategy against localization attacks

By Reza Shokri, George Theodorakopoulos, Carmela Troncoso, Jean-Pierre Hubaux, J. L. Boudec. 2012

433 citations recorded by the source corpus.

The mainstream approach to protecting the location-privacy of mobile users in location-based services (LBSs) is to alter the users' actual locations in order to reduce the location information exposed to the service provider. The location obfuscation algorithm behind an effective location-privacy preserving mechanism (LPPM) must consider three fundamental elements: the privacy requirements of the users, the adversary's knowledge and capabilities, and the maximal tolerated service quality degradation stemming from the obfuscation of true locations. We propose the first methodology, to the best of our knowledge, that enables a designer to find the optimal LPPM for a LBS given each user's service quality constraints against an adversary implementing the optimal inference algorithm. Such LPPM is the one that maximizes the expected distortion (error) that the optimal adversary incurs in reconstructing the actual location of a user, while fulfilling the user's service-quality requirement. We formalize the mutual optimization of user-adversary objectives (location privacy vs. correctness of localization) by using the framework of Stackelberg Bayesian games. In such setting, we develop two linear programs that output the best LPPM strategy and its corresponding optimal inference attack. Our optimal user-centric LPPM can be easily integrated in the users' mobile devices they use to access LBSs. We validate the efficacy of our game theoretic method against real location traces. Our evaluation confirms that the optimal LPPM strategy is superior to a straightforward obfuscation method, and that the optimal localization attack performs better compared to a Bayesian inference attack.

Subfields: Artificial Intelligence, Computer Science Applications, Sociology and Political Science

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Citations in the corpus

Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples (2016, background): “Sharing of location information is often associated with serious privacy threats [3], [35], [36].”
Machine Learning with Membership Privacy using Adversarial Regularization (2018, background): “pect to a particular attack, without anticipating and preparing for the (strongest) attack against itself. The conﬂicting objectives of the defender and the adversary can be modeled as a privacy game [2, 24, 34, 46]. In our particular setting, while the adversary tries to get the maximum inference g”
Broadening the Scope of Differential Privacy Using Metrics (2013, background): “Some of these works [19,20,21] propose the use of the expectation of distance error of the attacker as the way to quantify the privacy offered by a mechanism.”
Quantifying and protecting location privacy (2015, positive): “We show that this game can be solved using the following linear program [24].”
AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning (2018, methodology): “One category of defenses (e.g., [13– 16]) against general inference attacks leverage game theory.”
Geo-indistinguishability: differential privacy for location-based systems (2012, methodology): “As already stated, we will use the metrics for privacy and for the quality of service proposed in [36].”
Differentially Private Data Generative Models (2018, background): “In practice, differentially private schemes are often tailored to the spatio-temporal location privacy analysis [9, 45, 53, 58, 60].”
Predicting Users' Motivations behind Location Check-Ins and Utility Implications of Privacy Protection Mechanisms (2015, background): “provide a framework [30] to quantify location privacy, and a game-theoretic methodology [31] to optimize location privacy while respecting users’ utility requirements.”
Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons (2016, background): “Therefore, to address the privacy issues, collaborative localization in the future work requires designing a specific secured protocol for location share [78], [89], [90].”