面向隐私保护的非聚合式数据共享综述

Survey on privacy protection in non-aggregated data sharing

海量数据价值虽高但与用户隐私关联也十分密切,以高效安全地共享多方数据且避免隐私泄露为目标,介绍了非聚合式数据共享领域的研究发展。首先,简述安全多方计算及其相关技术,包括同态加密、不经意传输、秘密共享等;其次,分析联邦学习架构,从源数据节点和通信传输优化方面探讨现有研究;最后,整理对比面向隐私保护的非聚合式数据共享框架,为后续研究方案构建和运行提供支撑。此外,总结提出非聚合式数据共享领域的挑战和潜在的研究方向,如复杂多参与方场景、优化开销平衡、相关安全隐患等。

Although there is a great value hidden in the massive data,it can also easily expose user privacy.Aiming at efficiently and securely sharing data from multiple parties and avoiding leakage of user private information,the development of related research and technologies on the non-aggregated data sharing field was introduced.Firstly,secure multi-party computing and its technologies were briefly described,including homomorphic encryption,oblivious transfer,secret sharing,etc.Secondly,the federated learning architecture was analyzed from the aspects of source data nodes and transmission optimization.Finally,the existing non-aggregated data sharing frameworks were listed and compared.In addition,the challenges and future potential research directions were summarized,such as complex multi-party scenarios,the balance between optimization and cost,as well as related security risks.