arXiv Analytics

Sign in

arXiv:1905.07598 [cs.CR]AbstractReferencesReviewsResources

On the Privacy Guarantees of Gossip Protocols in General Networks

Richeng Jin, Yufan Huang, Huaiyu Dai

Published 2019-05-18, updated 2021-02-05Version 2

Recently, the privacy guarantees of information dissemination protocols have attracted increasing research interests, among which the gossip protocols assume vital importance in various information exchange applications. In this work, we study the privacy guarantees of gossip protocols in general networks in terms of differential privacy and prediction uncertainty. First, lower bounds of the differential privacy guarantees are derived for gossip protocols in general networks in both synchronous and asynchronous settings. The prediction uncertainty of the source node given a uniform prior is also determined. For the private gossip algorithm, the differential privacy and prediction uncertainty guarantees are derived in closed form. Moreover, considering that these two metrics may be restrictive in some scenarios, the relaxed variants are proposed. It is found that source anonymity is closely related to some key network structure parameters in the general network setting. Then, we investigate information spreading in wireless networks with unreliable communications, and quantify the tradeoff between differential privacy guarantees and information spreading efficiency. Finally, considering that the attacker may not be present at the beginning of the information dissemination process, the scenario of delayed monitoring is studied and the corresponding differential privacy guarantees are evaluated.

Related articles: Most relevant | Search more
arXiv:1612.02298 [cs.CR] (Published 2016-12-07)
Individual Differential Privacy: A Utility-Preserving Formulation of Differential Privacy Guarantees
arXiv:2207.07816 [cs.CR] (Published 2022-07-16)
Sotto Voce: Federated Speech Recognition with Differential Privacy Guarantees
arXiv:2304.02959 [cs.CR] (Published 2023-04-06)
When approximate design for fast homomorphic computation provides differential privacy guarantees