论文标题
章鱼：贷款堆叠的隐私协作评估
Octopus: Privacy-Preserving Collaborative Evaluation of Loan Stacking
论文作者
论文摘要
随着在线贷方的兴起，贷款堆叠问题已成为金融行业的重大问题。与之抗争的关键步骤之一是查询同行贷款人借款人的贷款历史。这在没有值得信赖的信贷局的市场中尤其重要。为了保护参与者的隐私和业务利益，我们希望隐藏借款人身份并从贷款发起人中隐藏数据，同时核实借款人是否授权查询。在本文中，我们提出了章鱼，这是一个分布式系统，用于执行查询，同时满足上述所有安全要求。从理论上讲，章鱼是合理的。实际上，它集成了多个优化以减少通信和计算开销。评估表明，章鱼可以在800个地理分布式服务器上运行，并且可以平均在0.5秒内执行查询。
With the rise of online lenders, the loan stacking problem has become a significant issue in the financial industry. One of the key steps in the fight against it is the querying of the loan history of a borrower from peer lenders. This is especially important in markets without a trusted credit bureau. To protect participants privacy and business interests, we want to hide borrower identities and lenders data from the loan originator, while simultaneously verifying that the borrower authorizes the query. In this paper, we propose Octopus, a distributed system to execute the query while meeting all the above security requirements. Theoretically, Octopus is sound. Practically, it integrates multiple optimizations to reduce communication and computation overhead. Evaluation shows that Octopus can run on 800 geographically distributed servers and can perform a query within about 0.5 seconds on average.
