Hacking social network data mining

Abstract

Over the years social network data has been mined to predict individuals' traits such as intelligence and sexual orientation. While mining social network data can provide many beneficial services to the user such as personalized experiences, it can also harm the user when used in making critical decisions such as employment. In this work, we investigate the reliability of applying data mining techniques on social network data to predict various individual traits. In spite of the preliminary success of such data mining applications, in this paper, we demonstrate the vulnerabilities of existing state of the art social network data mining techniques when they are facing malicious attacks. Our results indicate that making critical decisions, such as employment or credit approval, based solely on social network data mining results is still premature at this stage. Specifically, we explore Facebook likes data for predicting the traits of a Facebook user, including their political views and sexual orientation. We perform several types of malicious attacks on the predictive models to measure and understand their potential vulnerabilities. We find that existing predictive models built on social network data can be easily manipulated and suggest some countermeasures to prevent some of the proposed attacks.