Protecting Online Social Networks Profiles by Hiding Sensitive Data Attributes

Abstract

Online Social Networks (OSNs) have become a mainstream cultural phenomenon for millions of Internet users. More importantly, OSNs expose now information from multiple social spheres e.g. personal information or professional activity. We identify two stakeholders in online social networks: the OSN users and the OSN itself. On one hand, OSN users share an astonishing amount of information ranging from personal to professional. On the other hand, OSN services handle users’ information and manage all users’ activities in the network, being responsible for the correct functioning of its services and maintaining a profitable business model. Indirectly, this translates into ensuring that their users continue to happily use their services without becoming victims of malicious actions. We thus classify online social networks privacy and security issues into two categories of attacks on users and OSN. In this paper we propose a utility based association rule hiding algorithm for privacy preserving user profiles data against attacks from OSN users or even OSN applications. Experimental has been conducted on samples of real datasets. Experimental has been showed less attribute modification in the released user's profiles datasets.