Preserving Privacy in Social Networks Against Neighborhood Attacks

TLDR

Social network data publication raises privacy concerns, as local knowledge can enable re‑identification attacks, yet prior privacy methods address only relational data, not social networks. This paper initiates privacy preservation for social network data by focusing on neighborhood attacks. The authors demonstrate the challenge of neighborhood attacks and propose a practical countermeasure. Empirical results show that the anonymized networks support accurate aggregate query answering.

Abstract

Recently, as more and more social network data has been published in one way or another, preserving privacy in publishing social network data becomes an important concern. With some local knowledge about individuals in a social network, an adversary may attack the privacy of some victims easily. Unfortunately, most of the previous studies on privacy preservation can deal with relational data only, and cannot be applied to social network data. In this paper, we take an initiative towards preserving privacy in social network data. We identify an essential type of privacy attacks: neighborhood attacks. If an adversary has some knowledge about the neighbors of a target victim and the relationship among the neighbors, the victim may be re-identified from a social network even if the victim's identity is preserved using the conventional anonymization techniques. We show that the problem is challenging, and present a practical solution to battle neighborhood attacks. The empirical study indicates that anonymized social networks generated by our method can still be used to answer aggregate network queries with high accuracy.