移动社交网络中矩阵混淆加密交友隐私保护策略

随着移动设备和在线社交网络的快速发展，通过用户的个人属性配置文件匹配，能够帮助用户在邻近的社交网络中迅速找到和自己共同特征的朋友.然而，交友匹配很有可能泄漏用户的敏感信息，因此用户隐私得不到保障.提出一种移动社交网络中交友匹配过程中的隐私保护协议，用户利用混淆矩阵变换算法和内积计算实现交友过程中的隐私安全和高效的匹配；用户可以细粒度定义自己特征属性的特征权重，从而使匹配结果更精确.此外，利用机会分析模型模拟真实交友场景来保证交友的有效性.安全性分析表明，提出的方法更具有隐私性、可用性和更低的通信和计算开销.通过结合真实的社会网络数据进行测试和评估，对比结果显示，比现有解决方案更有效.

Privacy Preserving Friend Discovery of Matrix Confusion Encryption in Mobile Social Networks

With the rapid developments of mobile devices and online social networks, users of mobile social networks (MSNs) can easily discover and make new social interactions with others by profiles matching. However, personal profiles usually contain sensitive information of individuals, while the emerging requirement of profile matching in proximity mobile social networks may occasionally leak the sensitive information and hence violate people''s privacy. A profile matching protocol in MSNs is proposed, users utilize the confusion matrix transformation algorithm and dot product to achieve secure and efficient matching results; at the same time, users can customize the matching metrics to involve their own matching preference and to make the matching results more precise. In addition, opportunistic computing is adopted to simulate the real friend making senario to guarantee the effectiveness. Security analysis shows that the proposed scheme possesses higher privacy, serviceability, and lower computation and communication cost. Assessed by real social network data, the results demonstrate that the proposed scheme is superior to the existing works.