[论文解读] Prime Match: A Privacy-Preserving Inventory Matching System

Antigoni Polychroniadou, Gilad Asharov+7 more|arXiv (Cornell University)|Oct 14, 2023

Cryptography and Data SecurityComputer Science被引用 3

一句话总结

Prime Match 引入了一种用于金融交易的隐私保护库存匹配系统，使客户能够与银行在不泄露敏感订单细节的情况下私密匹配买入和卖出订单。该系统采用一种新颖的两轮安全线性比较协议，具备抗恶意攻击的安全性，确保除非发生匹配，否则不会泄露任何信息。这是首个在传统金融领域大规模生产环境中部署的 SMC 解决方案，目前在摩根大通运行。

ABSTRACT

Inventory matching is a standard mechanism/auction for trading financial stocks by which buyers and sellers can be paired. In the financial world, banks often undertake the task of finding such matches between their clients. The related stocks can be traded without adversely impacting the market price for either client. If matches between clients are found, the bank can offer the trade at advantageous rates. If no match is found, the parties have to buy or sell the stock in the public market, which introduces additional costs. A problem with the process as it is presently conducted is that the involved parties must share their order to buy or sell a particular stock, along with the intended quantity (number of shares), to the bank. Clients worry that if this information were to leak somehow, then other market participants would become aware of their intentions and thus cause the price to move adversely against them before their transaction finalizes. We provide a solution, Prime Match, that enables clients to match their orders efficiently with reduced market impact while maintaining privacy. In the case where there are no matches, no information is revealed. Our main cryptographic innovation is a two-round secure linear comparison protocol for computing the minimum between two quantities without preprocessing and with malicious security, which can be of independent interest. We report benchmarks of our Prime Match system, which runs in production and is adopted by J.P. Morgan. The system is designed utilizing a star topology network, which provides clients with a centralized node (the bank) as an alternative to the idealized assumption of point-to-point connections, which would be impractical and undesired for the clients to implement in reality. Prime Match is the first secure multiparty computation solution running live in the traditional financial world.

研究动机与目标

• 解决传统库存匹配中客户订单细节向银行及潜在其他市场参与者泄露隐私的问题。
• 在不泄露订单数量或标的符号的情况下，实现客户与银行之间的安全、私密匹配。
• 设计一种实用、可投入生产的系统，支持在真实网络环境约束下的银行对客户以及客户对客户的匹配。
• 开发一种两轮安全比较协议，具备抗恶意攻击的安全性，实现金融场景下对最小数量的高效私密计算。
• 在真实金融机构中部署一个完全运行的系统，证明安全多方计算在生产环境中的可行性和性能。

提出的方法

• 系统采用星型拓扑网络，由银行作为协调方，无需客户之间直接建立点对点连接，实现实际部署。
• 私密订单注册在客户端浏览器中完成，订单（标的、方向、数量）在上传前被加密，确保数据从不离开客户设备。
• 采用两轮安全线性比较协议，无需预处理即可计算两个私有数量的最小值，提供抗恶意攻击的安全性，并实现高效匹配。
• 协议仅在匹配时揭示匹配数量和固定价差；其他所有数据在无匹配时仍保持私密。
• 系统分两个阶段处理订单：首先评估阈值数量以检测潜在匹配；仅当发现匹配时，才揭示完整数量。
• 该应用每 30 分钟在配备 32 个 vCPU 的生产平台上运行一次，支持最多 5000 个标的和每场拍卖 10 至 60 名客户。

Figure 5: Prime Match User Interface of a completed matching run.

实验结果

研究问题

• RQ1 能否设计一种安全多方计算协议，实现在金融市场上对买入和卖出订单的私密匹配，同时确保不泄露任何私有数据？
• RQ2 如何构建一种两轮安全比较协议，实现对两个私有值的最小值计算，具备抗恶意攻击的安全性且无需预处理？
• RQ3 在真实世界、高频金融环境中部署安全多方计算系统时，需要在架构和密码学之间做出哪些权衡？
• RQ4 如何在基于浏览器的客户端计算模型中实现私密订单注册与匹配，以确保数据从不离开客户设备？
• RQ5 在大型金融机构中对这类系统进行生产环境部署时，可实现怎样的性能和可扩展性特征？

主要发现

• Prime Match 是首个在传统金融行业生产环境中实际部署的隐私计算系统，自 2021 年 9 月起在摩根大通运行。
• 该系统在无匹配时实现安全匹配且无任何信息泄露，仅在发生匹配时揭示匹配数量和固定价差。
• 具备抗恶意攻击安全性的两轮安全线性比较协议高效且实用，支持每 30 分钟一次的实时匹配，完成时间低于 55 秒。
• 性能基准测试显示，系统可支持最多 5000 个标的和每场拍卖 10 至 60 名客户，完整数量仅在成功匹配后才被揭示。
• 系统采用基于浏览器的客户端计算模型部署，确保所有私有数据始终留在客户设备上，从未传输至服务器。
• 该实现成功应对了真实世界中的挑战，如网络拓扑、延迟以及与现有银行基础设施的集成，证明了 SMC 在生产金融环境中的可行性。

Figure 6: Prime Match system architecture connecting external users/clients to the bank’s network.