All liaisons are dangerous when all your friends are known to us

TL;DR

This paper presents a new user profiling algorithm for social networks, demonstrating privacy risks from sharing contacts and proposing measures to mitigate privacy leaks from social graph data mining.

Contribution

Introduces a novel user profiling algorithm and analyzes privacy risks associated with sharing social contacts in online social networks.

Findings

The profiling algorithm achieves high accuracy in user attribute prediction.

Sharing contacts can significantly compromise user privacy.

Proposed measures can reduce privacy leaks from social graph analysis.

Abstract

Online Social Networks (OSNs) are used by millions of users worldwide. Academically speaking, there is little doubt about the usefulness of demographic studies conducted on OSNs and, hence, methods to label unknown users from small labeled samples are very useful. However, from the general public point of view, this can be a serious privacy concern. Thus, both topics are tackled in this paper: First, a new algorithm to perform user profiling in social networks is described, and its performance is reported and discussed. Secondly, the experiments --conducted on information usually considered sensitive-- reveal that by just publicizing one's contacts privacy is at risk and, thus, measures to minimize privacy leaks due to social graph data mining are outlined.