Profile Matching Across Unstructured Online Social Networks: Threats and Countermeasures

TL;DR

This paper demonstrates that user profiles across unstructured online social networks can be matched with high accuracy using publicly shared attributes, highlighting privacy risks and proposing countermeasures that balance privacy and utility.

Contribution

It introduces a machine learning-based framework for profile matching without relying on network structure and explores privacy-utility tradeoffs with optimization strategies.

Findings

Profiles can be matched with high probability using shared attributes.

Countermeasures can reduce attack success at the cost of profile utility.

The framework is validated on real-life datasets.

Abstract

In this work, we propose a profile matching (or deanonymization) attack for unstructured online social networks (OSNs) in which similarity in graphical structure cannot be used for profile matching. We consider different attributes that are publicly shared by users. Such attributes include both obvious identifiers such as the user name and non-obvious identifiers such as interest similarity or sentiment variation between different posts of a user in different platforms. We study the effect of using different combinations of these attributes to the profile matching in order to show the privacy threat in an extensive way. Our proposed framework mainly relies on machine learning techniques and optimization algorithms. We evaluate the proposed framework on two real-life datasets that are constructed by us. Our results indicate that profiles of the users in different OSNs can be matched with high probability by only using publicly shared attributes and without using the underlying graphical structure of the OSNs. We also propose possible countermeasures to mitigate this threat in the expense of reduction in the accuracy (or utility) of the attributes shared by the users. We formulate the tradeoff between the privacy and profile utility of the users as an optimization problem and show how slight changes in the profiles of the users would reduce the success of the attack. We believe that this work will be a valuable step to build a privacy-preserving tool for users against profile matching attacks between OSNs.