中文摘要：

基于邻域的社会化推荐需要同时依赖用户的历史行为数据和完善的社交网络拓扑图,但通常这些数据分别属于不同平台,如推荐系统服务提供商和社交网络服务提供商。出于维护自身数据价值及保护用户隐私的考虑,他们并不愿意将数据信息提供给其他方。针对这一现象,提出了2种数据隐私保护的社会化推荐协议,可以在保护推荐系统服务提供商和社交网络服务提供商的数据隐私的同时,为用户提供精准的推荐服务。其中,基于不经意传输的社会化推荐,计算代价较小,适用于对推荐效率要求较高的应用;基于同态加密的社会化推荐,安全程度更高,适用于对数据隐私要求较高的应用。在4组真实数据集上的实验表明,提出的2种方案切实可行,用户可以根据自身需求选择合适的方案。

英文摘要：

Social recommendation is a method which requires the participants of both user＇s historical behavior data and social network, which generally belong to different parties, such as recommendation system service provider and social network service provider. Considering the fact that in order to maintain the value of their own data interests and user＇s privacy, none of them will provide data information to the other, two privacy preserving protocols are proposed for efficient computation of social recommendation which needs the cooperation of two parties（recommendation system service provider and social network service provider）. Both protocols enable two parties to compute the social recommendation without revealing their private data to each other. The protocol based on the well-known oblivious transfer multiplication has a low cost, and is suitable for the application of high efficiency requirements. And the one based on homomorphic cryptosystem has a better privacy preserving, and is more suitable for the application of higher data privacy requirements. Experimental results on the four real datasets show those two protocols are efficient and practical. Users are suggested to choose the appropriate protocol according to their own need.