You are Who You Know and How You Behave: Attribute Inference Attacks via Users' Social Friends and Behaviors

TL;DR

This paper introduces novel privacy attacks that combine social network data and behavioral information to accurately infer users' private attributes, highlighting serious privacy risks in online social networks.

Contribution

The paper develops a new model integrating social friends and behavioral records for attribute inference, demonstrating its effectiveness through theoretical analysis and large-scale experiments.

Findings

Attack correctly infers user locations with 57% success rate.

Success rate increases to over 90% when attacking half of the users.

Our method infers attributes for more users than previous approaches.

Abstract

We propose new privacy attacks to infer attributes (e.g., locations, occupations, and interests) of online social network users. Our attacks leverage seemingly innocent user information that is publicly available in online social networks to infer missing attributes of targeted users. Given the increasing availability of (seemingly innocent) user information online, our results have serious implications for Internet privacy -- private attributes can be inferred from users' publicly available data unless we take steps to protect users from such inference attacks. To infer attributes of a targeted user, existing inference attacks leverage either the user's publicly available social friends or the user's behavioral records (e.g., the webpages that the user has liked on Facebook, the apps that the user has reviewed on Google Play), but not both. As we will show, such inference attacks achieve limited success rates. However, the problem becomes qualitatively different if we consider both social friends and behavioral records. To address this challenge, we develop a novel model to integrate social friends and behavioral records and design new attacks based on our model. We theoretically and experimentally demonstrate the effectiveness of our attacks. For instance, we observe that, in a real-world large-scale dataset with 1.1 million users, our attack can correctly infer the cities a user lived in for 57% of the users, via confidence estimation, we are able to increase the attack success rate to over 90% if the attacker selectively attacks a half of the users. Moreover, we show that our attack can correctly infer attributes for significantly more users than previous attacks.