SocialMix: Supporting Privacy-Aware Trusted Social Networking Services

Online Social Networks (OSNs) have been one of the most successful web-based communication models. In the recent years, a new category of OSNs namely anonymous social networks are becoming popular. Unlike traditional Online Social Networks, anonymous social networks allow users to communicate without exposing their identity. This paper presents a trusted anonymous social network service that can anonymize user identities during interaction even though the communication happens with the user's own trusted friends and contacts on the social network. A fundamental requirement of such a trusted anonymous social networks is to protect the user's identity under the guarantees of anonymity. However, in existing approaches, even though the user information is anonymized, by continuously aggregating the information from the messages posted by a user, it is possible to re-identify the user with high probability. In this paper, we propose SocialMix that anonymizes the users of a trusted social network such that the aggregation of messages can be prevented. We make three original contributions. First, we develop the SocialMix model for trusted anonymous social networks so that communication privacy can be protected by k-anonymization. Second, by considering the features of OSNs, we analyze the vulnerabilities of the naive methods that might be exploited to break the privacy. We develop new techniques to improve the attack-resilience of the SocialMix approach. Third, we propose intelligent mix node selection methods to significantly reduce the required number of social mix nodes while still keeping high anonymization rate. Our experiments shows that SocialMix provides high attack resilience and keeps high anonymization rate with few mix nodes under the trusted social network model.