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PRIVIC: A privacy-preserving method for incremental collection of location data

Sayan Biswas
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Catuscia Palamidessi
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With recent advancements in technology, the threats of privacy violations of individuals' sensitive data are surging. Location data, in particular, have been shown to carry a substantial amount of sensitive information. A standard method to mitigate the privacy risks for location data consists in adding noise to the true values to achieve geo-indistinguishability. However, we argue that geo-indistinguishability alone is not sufficient to cover all privacy concerns. In particular, isolated locations are not sufficiently protected by the state-of-the-art Laplace mechanism (LAP) for geoindistinguishability. In this paper, we focus on a mechanism that can be generated by using the Blahut-Arimoto algorithm (BA) from rate-distortion theory. We show that the BA mechanism, in addition to providing geo-indistinguishability, enforces an elastic metric that mitigates the problem of isolation. We then proceed to study the utility of BA in terms of the precision of the statistics that can be derived from the reported data, focusing on the inference of the original distribution. To this purpose, we apply the iterative Bayesian update (IBU), an instance of the famous expectation-maximization method from statistics, that produces the most likely distribution for any obfuscation mechanism. We show that BA harbours a better statistical utility than LAP for high levels of privacy, and becomes comparable as the level of privacy decreases. Remarkably, we point out a surprising connection, namely that BA and IBU, two apparently unrelated methods that were developed for completely different purposes, are dual to each other. Exploiting this duality and the privacy-preserving properties of BA, we propose an iterative method, PRIVIC, for a privacy-friendly incremental collection of location data from users by service providers. In addition to extending the privacy guarantees of geo-indistinguishability and retaining a better statistical utility than LAP, PRIVIC also provides an optimal trade-off between information leakage and quality of service. We illustrate the soundness and functionality of our method both analytically and with experiments.
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hal-03968692 , version 1 (01-02-2023)


  • HAL Id : hal-03968692 , version 1


Sayan Biswas, Catuscia Palamidessi. PRIVIC: A privacy-preserving method for incremental collection of location data. 2022. ⟨hal-03968692⟩
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