Bilkent University
Department of Computer Engineering


Profile Matching Across Online Social Networks


Volkan Küçük
MS Student
Computer Engineering Department
Bilkent University

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 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 a real-life dataset that is 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 believe that this work will be a valuable step to build a privacy-preserving tool for users against profile matching attacks between OSNs.


DATE: 10 October, 2016, Monday @ 15:40