Profile Matching Across Online Social Networks

TL;DR

This paper investigates privacy risks in online social networks by demonstrating how user profiles can be matched across platforms using publicly shared attributes and machine learning, highlighting significant privacy threats.

Contribution

It introduces a comprehensive framework that combines various user attributes and network structure to improve profile matching accuracy across OSNs, surpassing existing methods.

Findings

High probability of profile matching using shared attributes

Framework outperforms state-of-the-art in precision

Privacy risks are significant due to publicly available information

Abstract

In this work, we study the privacy risk due to profile matching across online social networks (OSNs), in which anonymous profiles of OSN users are matched to their real identities using auxiliary information about them. We consider different attributes that are publicly shared by users. Such attributes include both strong identifiers such as user name and weak identifiers such as interest or sentiment variation between different posts of a user in different platforms. We study the effect of using different combinations of these attributes to profile matching in order to show the privacy threat in an extensive way. The proposed framework mainly relies on machine learning techniques and optimization algorithms. We evaluate the proposed framework on three datasets (Twitter - Foursquare, Google+ - Twitter, and Flickr) and show how profiles of the users in different OSNs can be matched with high probability by using the publicly shared attributes and/or the underlying graphical structure of the OSNs. We also show that the proposed framework notably provides higher precision values compared to state-of-the-art that relies on machine learning techniques. We believe that this work will be a valuable step to build a tool for the OSN users to understand their privacy risks due to their public sharings.