Preserving Link Privacy in Social Network Based Systems
This work addresses privacy concerns for users in social network systems, though it appears incremental as it builds on existing perturbation techniques.
The paper tackles the problem of exposing users' sensitive trust relationships in social network-based systems by proposing an algorithm that perturbs the social graph to provide link privacy, with evaluation on real-world graphs showing a slight reduction in utility.
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.