| 引用本文: | 马旭阳,周小凯,郑浩宇,崔斌,徐泉清,杨传辉,晏潇,江佳伟.基于无服务器计算的多方数据库安全计算系统.软件学报,2025,36(3):1084-1106 |
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| 基于无服务器计算的多方数据库安全计算系统 |
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马旭阳1, 周小凯1, 郑浩宇1, 崔斌2, 徐泉清3, 杨传辉3, 晏潇4, 江佳伟1
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1.武汉大学 计算机学院, 湖北 武汉 430072;2.北京大学 计算机学院, 北京 100971;3.蚂蚁集团, 浙江 杭州 310013;4.香港中文大学 博智感知交互研究中心, 香港 999077
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| 摘要: |
| 联合多方数据库的安全计算可以在保护数据隐私的情况下, 对多个数据库的私有数据进行联合查询或联合建模. 这样的联合体通常是一个松散的组织, 各参与的数据库可以随时离线, 然而现有多方安全计算系统通常采用秘密共享等隐私计算方案, 需要参与者保持在线状态, 导致系统的可用性较差. 此外, 现有系统对外提供服务时无法预知用户的数量以及请求速度, 如果将系统部署在私有集群或者租用云计算平台的虚拟机, 面对爆发式请求时系统延迟增大, 在请求较少时又造成资源浪费, 表现出较差的可扩展性. 随着云计算技术的发展, 无服务器计算(serverless computing)作为一种新的云原生部署范式出现, 具有良好的弹性资源伸缩能力. 在该工作中, 提出了基于无服务器计算环境的系统架构和间接通信方案, 实现了一套高可扩展、高可用的多方数据库安全计算系统, 可以容忍数据库节点掉线, 并且在用户请求流量发生变化时自动伸缩系统资源. 基于阿里云和OceanBase数据库实现了系统原型并进行了充分的实验对比, 结果显示该系统在低频查询、横向建模等任务上, 在计算成本、系统性能和可扩展性方面优于现有系统, 最高能够节省78%的计算成本、提升系统性能1.6倍, 同时也分析了本系统对于复杂查询、纵向建模等任务存在的不足. |
| 关键词: 云原生系统 无服务器计算 多方数据库安全计算 |
| DOI：10.13328/j.cnki.jos.007283 |
| 分类号:TP311 |
| 基金项目:国家重点研发计划(2023YFB2703604); 湖北省重点研发计划 (2023BAB077, 2023BAB170); 国家自然科学基金 (62472327); 中央高校基本科研业务费专项资金 (2042023kf0219); CCF-蚂蚁科研基金 (CCF-AFSG RF20230106) |
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| Secure Multi-party Database Computing System Based on Serverless Computing |
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MA Xu-Yang1, ZHOU Xiao-Kai1, ZHENG Hao-Yu1, CUI Bin2, XU Quan-Qing3, YANG Chuan-Hui3, YAN Xiao4, JIANG Jia-Wei1
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1.School of Computer Science, Wuhan University, Wuhan 430072, China;2.School of Computer Science, Peking University, Beijing 100871, China;3.Ant Group, Hangzhou 310013, China;4.Centre for Perceptual and Interactive Intelligence, The Chinese University of Hong Kong, Hong Kong 999077, China
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| Abstract: |
| Secure computation of federated multi-party databases can perform federated querying or federated modeling on private data from multiple databases while preserving data privacy. Such a federation is typically a loosely organized group where the participating databases may dropout unexpectedly. However, existing multi-party secure computation systems usually employ privacy-preserving computation schemes like secret sharing, which require participants to remain online, resulting in poor system availability. Moreover, these systems are unable to predict the number of users or request rates when providing services externally. If the system is deployed on a private cluster or rented virtual machines from a cloud computing platform, it will experience increased latency during sudden bursts of requests and resource waste when the request workload is low, leading to poor overall scalability of the system. With the advancement of cloud computing technology, serverless computing has emerged as a new cloud-native deployment paradigm that offers excellent elastic resource scaling. This study designs a system architecture and an indirect communication scheme within the serverless computing framework to architect a highly scalable and highly available multi-party database secure computation system. This system can tolerate database node disconnections and automatically scale system resources in response to user request traffic changes. A system prototype based on Alibaba Cloud and OceanBase database is implemented. Comprehensive experimental comparisons are conducted. The results show that the proposed system outperforms existing systems in terms of computational cost, system performance, and scalability for tasks such as low-frequency queries and horizontal modeling. It can save up to 78% in computational costs and improve system performance by over 1.6 times. The shortcomings of the proposed system for tasks such as complex queries and vertical modeling are analyzed. |
| Key words: cloud-native system serverless computing secure multi-party database computing |
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