基于混淆布鲁姆过滤器的云外包隐私集合比较协议

针对基于混淆布鲁姆过滤器的隐私集合比较（PSI）协议中存在参与方信息获取不对等及协议不能有效应用于云环境等问题，将混淆布鲁姆过滤器算法与代理不经意传输协议相结合，提出了一种基于混淆布鲁姆过滤器和代理不经意传输的云外包隐私集合比较协议。首先，该算法通过引入混淆布鲁姆过滤器的概念，解决了传统标准布鲁姆过滤器产生误判的问题，进而达到高效存储和传输大数据的目的；其次，采用代理不经意传输协议，能够将复杂耗时的计算外包给云代理服务器，使得云租户不需实时在线、仅需进行少量计算；最后，在云外包隐私集合比较过程中，云租户间无需交互，能够公平地得到集合比较结果。理论分析和性能对比表明，该算法的通信复杂度和计算复杂度是线性的，并且协议是安全和有效的。

Cloud outsourcing private set intersection protocol based on garbled Bloom filter

Focusing on the issues that information acquired by different participants are not equal in the Private Set Intersection (PSI) protocol based on Garbled Bloom Filter (GBF), which can not be effectively applied to the cloud environment, a cloud outsourcing PSI protocol combined the garbled Bloom filter algorithm with the proxy oblivious transfer protocol was proposed. Firstly, by introducing the garbled Bloom filter, the problem of false positive in the traditional standard Bloom filter was solved to achieve efficient storage and large data transmission. Secondly, the complex time-consuming computation could be outsourced to the cloud proxy server by using proxy oblivious transfer protocol, so that the cloud tenants did not need to be online in real-time and only needed a small amount of computation. Finally, in the processing of the cloud outsourcing privacy set intersection, the comparison results could be fairly obtained without the interaction among the cloud tenants. Theoretical analysis and performance comparison show that the communication and computation complexities of the proposed protocol are linear, and the proposed protocol is safe and effective.