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Community detection is an important aspect of social network analysis, but social factors such as user intimacy, influence, and user interaction behavior are often overlooked as important factors. Most of the existing methods are single classification algorithms; multi-classification algorithms that can discover overlapping communities are still incomplete. In former works, we calculated intimacy based on the relationship between users, and divided them into their social communities based on intimacy. However, a malicious user can obtain the other user relationships, thus to infer other users interests, and even pretend to be the another user to cheat others. Therefore the information users concerned about needs to be transferred in the manner of privacy protection. In this paper, we propose an efficient privacy preserving algorithm to preserve the privacy of information in social networks. First, during expansion of communities on the base of mining seed, in order to prevent others from malicious users, we verify their identities after they send a request. We make use of the recognition and nontampering of the block chain to store the user's public key and bind to the block address, which is used for authentication. At the same time, in order to prevent the honest but curious users from illegal access to other users' information, we do not send plaintext directly after the authentication, but hash the attributes by mixed hash encryption to make sure that users can only calculate the matching degree rather than know specific information of other users. Analysis shows that our protocol would serve well against different types of attacks.

Authentication With Block-Chain Algorithm and Text Encryption Protocol in Calculation of Social Network