Survey on privacy protection in non-aggregated data sharing - Youhuizi LI; Yuyu YIN; Honghao GAO; Yi JIN; Xinheng WANG (Tongxin xuebao)
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.