Fine-Grained Encountering Information Collection under Neighbor Anonymity in Mobile Opportunistic Social Networks

In mobile opportunistic social networks (MOSNs), mobile devices carried by people communicate with each other directly when they meet for proximity-based MOSN services (e.g., file sharing) without the support of infrastructures. In current methods, when nodes meet, they simply communicate with their real IDs, which leads to privacy and security concerns. Anonymizing real IDs among encountering neighbor nodes solves such concerns. However, this prevents nodes from collecting real ID based encountering information, which is needed to support MOSN services. Therefore, in this paper, we propose FaceChange that can support both anonymizing real IDs among neighbor nodes and collecting real ID based encountering information. To realize neighbor node anonymity, two encountering nodes communicate anonymously. Then, when the two nodes disconnect, each node forwards an encrypted encountering evidence to the encountered node to enable encountering information collection. A set of novel schemes are designed to protect the confidentiality and uniqueness of encountering evidences. FaceChange also supports fine-grained control over what encountering information should be forwarded based on attribute similarity (i.e., trust) without disclosing attributes. Extensive analysis and experiments show the effectiveness of FaceChange on protecting node privacy and meanwhile supporting the encountering information collection in MOSNs. Real implementation on smartphones also demonstrates its energy efficiency.