PRIVIC: A privacy-preserving method for incremental collection of location data
Sayan Biswas, Catuscia Palamidessi

TL;DR
PRIVIC introduces a novel privacy-preserving mechanism for incremental location data collection that combines the Blahut-Arimoto algorithm with iterative Bayesian updating to enhance privacy and utility.
Contribution
The paper proposes PRIVIC, a new method that integrates BA and IBU for improved privacy protection and statistical utility in location data collection.
Findings
BA enforces geo-ind and mitigates isolation issues.
IBU effectively de-noises data, improving utility.
PRIVIC outperforms Laplace mechanism at high privacy levels.
Abstract
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 (geo-ind). However, geo-ind 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 geo-ind. In this paper, we focus on a mechanism based on the Blahut-Arimoto algorithm (BA) from the rate-distortion theory. We show that BA, in addition to providing geo-ind, enforces an elastic metric that mitigates the problem of isolation. Furthermore, BA provides an optimal trade-off between information leakage and quality of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Probability and Risk Models · Data-Driven Disease Surveillance
