Aiming at privacy protection of user attribute matching in social networks, a privacy preserving user attribute matching method was proposed based on anonymous attribute-based encryption, which can be applied to centralized attribute matching scenarios. In this method, two attribute lists were used by each user to descript his own profile and the dating preference. Then, these two lists were encoded into attribute secret key and ciphertext access control strategy respectively for the purpose of privacy protection. Finally, the server made a matching decision by judging whether the ciphertext which implies the dating preference could be decrypted correctly by the secret key which implies the user profile. In this way, the server can achieve bidirectional attribute matching without knowing the specific attribute information of both sides. According to the analysis and experimental results, the proposed method has strong practicability because it can provide high computational efficiency while ensuring privacy security.

Concerning the problem of protecting user's location privacy in proximity testing, a new method of achieving fast proximity testing with privacy preserving was proposed. The map was divided with the grid by the proposed method. In the process of proximity testing, firstly, the vicinity region of the user was transformed into a collection of the surrounding grids. Then, the intersection of the users' vicinity regions was calculated by using the Private Set Intersection (PSI) for privacy preserving. Finally, the proximity determination was made based on whether the intersection was empty. The results of analysis and experiment show that, compared with the existing methods based on private equality testing and the method based on coordinate transformation, the proposed method can solve the fairness issue of privacy preserving in proximity testing, resist the collusion attack between the server and the user, and has a higher computational efficiency.