【Author】
Liu, Jingwei; He, Xinyu; Sun, Rong; Du, Xiaojiang; Guizani, Mohsen
【Source】IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021)
【Abstract】Each bank has different clients and each client may have transactions with multiple banks. Hence, clients' data in a single bank may be partial and incomplete. If the data can be combined, each bank obtains comprehensive information, so as to better carry out business and enhance the quality of service, such as recommending financial products and inquiring about personal credit records. However, after the promulgation of GDPR by European Union in 2018, it is illegal to directly consolidate data crossing enterprises due to privacy and security concerns, especially for privacy-sensitive industries. Emerging federated learning(FL) is very suitable for secure data sharing for distributed banks in privacy. To prevent from connection of clients' data and the certain bank, we adopt anonymity mechanism to hide the real identity of banks. In this paper, we first propose blockchain-empowered secure federated learning for distributed banks based on multi-party computation(MPC) with multi-key fully-homomorphic encryption(FHE) scheme. Then, we give detailed description of multi-key FHE based MPC protocol, anonymity mechanism and permissioned blockchain consensus protocol. Finally, we analyze the security and compare our scheme with several existed schemes. Numerical results show that the proposed data sharing scheme has good performance in terms of computational overhead and model accuracy.
【标题】通过金融许可区块链中的 MPC 与 FL 的隐私保护数据共享方案
【摘要】每家银行都有不同的客户，每个客户可能与多家银行进行交易。因此，客户在一家银行的数据可能不完整且不完整。如果能够将数据结合起来，各家银行就可以获得全面的信息，从而更好地开展业务，提高服务质量，例如推荐金融产品、查询个人信用记录等。但是，2018年欧盟颁布GDPR后，出于隐私和安全的考虑，直接跨企业整合数据是违法的，尤其是对隐私敏感的行业。新兴的联邦学习（FL）非常适合分布式银行在隐私方面的安全数据共享。为防止客户数据与某家银行发生关联，我们采用匿名机制隐藏银行的真实身份。在本文中，我们首先提出了基于多方计算（MPC）和多密钥全同态加密（FHE）方案的分布式银行区块链授权的安全联邦学习。然后，我们详细描述了基于多密钥 FHE 的 MPC 协议、匿名机制和许可区块链共识协议。最后，我们分析了安全性并将我们的方案与现有的几种方案进行了比较。数值结果表明，所提出的数据共享方案在计算开销和模型精度方面具有良好的性能。
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