Analysis-preserving protection of user privacy against information leakage of social-network Likes ?
Recent scientific results have shown that social network Likes, such as the Like Button records of Facebook, can be used to automatically and accurately predict even highly sensitive personal attributes. Although this could be the goal of a number of non-malicious activities, to improve products, services, and targeting, it represents a dangerous invasion of privacy with possible intolerable consequences. However, completely defusing the information power of Likes appears improper. In this paper, we propose a protocol able to keep Likes unlinkable to the identity of their authors, in such a way that the user may choose every time she expresses a Like, those non-identifying (even sensitive) attributes she wants to reveal. This way, analysis anonymously relating Likes to various characteristics of people is preserved, with no risk for users privacy. The protocol is shown to be secure and also ready to the possible future evolution of social networks towards P2P fully distributed models.
privacy social networks privacy-preserving data analysis partially blind signature