Profile Matching Across Online Social Networks
Anisa Halimi, Erman Ayday

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…
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