Private Social Network Data Sharing

TL;DR

This paper investigates privacy vulnerabilities in online social networks and proposes a framework to prevent malicious inference of sensitive user attributes, demonstrating its effectiveness in protecting privacy while maintaining usefulness.

Contribution

It introduces a novel privacy-preserving framework for social networks that defends against attribute inference attacks, addressing a gap in existing privacy protection methods.

Findings

Framework effectively prevents sensitive attribute inference

Maintains social network utility and usefulness

Demonstrates robustness against privacy attacks

Abstract

The increasing popularity of online social network brings huge privacy threat for the end users. While existing work focus on inferring sensitive attributes from the social network such as age, location and gender, little has been done on how to protect the users' privacy by preventing the malicious inference. In this paper we investigated the privacy vulnerability of the existing social network and designed a privacy-preserving framework. We evaluated the framework's privacy and usefulness guarantees, demonstrated its effectiveness on classification and the defense against the privacy attack.