A Probabilistic Privacy Preserving Strategy for Word-of-Mouth Social Networks

An online social network (OSN) is a platform that makes people communicate with friends, share messages, accelerate business, and enhance teamwork. In the OSN, privacy issues are increasingly concerned, especially in private message leaks in word-of-mouth. A user's privacy may be leaked out by acquaintances without user's consent. In this paper, an integrated system is designed to prevent this illegal privacy leak. In particular, we only use the method of space vector model to determine whether the user's private message is really leaked. Canary traps techniques are used to detect leakers. Then, we define a trust degree mechanism to evaluate trustworthiness of a communicator dynamically. Finally, we set up a new message publishing system to determine who can obtain the message of publisher. Secrecy performance analysis is provided to verify the effectiveness of the proposed message publishing system. Accordingly, a user in social networks can check whether other users are trustworthy before sending their private messages.