保护私有信息的社交网络合群判定

Privacy-preserving join decision in social networks

为了解决社交网络中用户申请加入群组的合适性判断问题,将安全多方计算技术中的求和协议与秘密比较协议相结合提出了保护私有信息的合群判定协议.其中基础协议解决一维线性模型下问题的安全求解,扩展协议对基于圆边界的多维模型情况进行判定.针对单一申请者与网络群组多用户的特点,将问题转换为两方计算模型可实现的形式,在证明了协议正确性的基础上分析协议的复杂度,并且利用安全视图的方法逐步验证了在协议执行过程中不会泄露任何个人的隐私数据.实际使用中协议能够有效地回避盲目的系统推荐和管理员离线所产生的判定时延,同时保护申请者和群组成员的隐私数据.

ABSTRACT In order to decide the appropriateness of a new user applying to join in a group in the social network, a privacy-preserving protocol was designed by the secure sum protocol and secure comparison protocol. In the privacy-preserving protocol, the secure basis protocol was devised for problems under a one-dimensional linear model, and the advanced protocol for a multi-dimensional model with a circular boundary. For the case of a single applicant and a multi-user group, the solution was converted and realized in a two-party computation model. After the proof of correctness, the complexity was discussed, and there is no leaking message during the process by the analysis of data views in each step. The privacy-preserving protocol avoids not only the blindness of auto recommendation by the net-system but also the decision delay due to the administrator＆#39;s offline. In the meanwhile, the privacy of the applicant and group members can be protected without leaking any information.