Preserving Link Privacy in Social Network Based Systems

TL;DR

This paper introduces a graph perturbation algorithm to protect users' trust relationships in social networks, balancing privacy with utility, and demonstrates its effectiveness across various secure system applications.

Contribution

It proposes a novel social graph perturbation algorithm for link privacy, introduces utility and privacy metrics, and evaluates its effectiveness on real social network data.

Findings

The algorithm effectively balances privacy and utility in social graphs.

Perturbed graphs maintain sufficient utility for secure system applications.

The approach enhances link privacy without significantly compromising social network functionality.

Abstract

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.