Preserving Link Privacy in Social Network Based Systems

By Prateek Mittal, Charalampos Papamanthou, Dawn Song. 2012

111 citations recorded by the source corpus.

A growing body of research leverages social network based trust relationships to improve the functionality of the system. However, these systems expose users' trust relationships, which is considered sensitive information in today's society, to an adversary. In this work, we make the following contributions. First, we propose an algorithm that perturbs the structure of a social graph in order to provide link privacy, at the cost of slight reduction in the utility of the social graph. Second we define general metrics for characterizing the utility and privacy of perturbed graphs. Third, we evaluate the utility and privacy of our proposed algorithm using real world social graphs. Finally, we demonstrate the applicability of our perturbation algorithm on a broad range of secure systems, including Sybil defenses and secure routing.

Subfields: Artificial Intelligence, Sociology and Political Science

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Citations in the corpus

Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples (2016, background): “Data privacy is an issue of critical importance, motivating perturbation of query results over sensitive datasets for protecting users’ privacy [5], [12], [26], [29], [31], [38].”
MLCapsule: Guarded Offline Deployment of Machine Learning as a Service (2018, result): “Concretely, we measure the Jensen-Shannon divergence between the original posterior (P ) and the noised one (P ′), denoted by JSD(P, P ′), following previous works [21, 4].”