On the effectiveness of obfuscation techniques in online social networks

Data obfuscation is a well-known technique for protecting user privacy against inference attacks, and it was studied in diverse settings, including search queries, recommender systems, location-based services and Online Social Networks (OSNs). However, these studies typically take the point of view of a single user who applies obfuscation, and focus on protection of a single target attribute. Unfortunately, while narrowing the scope simplifies the problem, it overlooks some significant challenges that effective obfuscation would need to address in a more realistic setting. First, correlations between attributes imply that obfuscation conducted to protect a certain attribute, may influence inference attacks targeted at other attributes. In addition, when multiple users conduct obfuscation simultaneously, the combined effect of their obfuscations may be significant enough to affect the inference mechanism to their detriment. In this work we focus on the OSN setting and use a dataset of 1.9 million Facebook profiles to demonstrate the severity of these problems and explore possible solutions. For example, we show that an obfuscation policy that would limit the accuracy of inference to 45% when applied by a single user, would result in an inference accuracy of 75% when applied by 10% of the users. We show that a dynamic policy, which is continuously adjusted to the most recent data in the OSN, may mitigate this problem. Finally, we report the results of a user study, which indicates that users are more willing to obfuscate their profiles using popular and high quality items. Accordingly, we propose and evaluate an obfuscation strategy that satisfies both user needs and privacy protection.