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面向数据联邦的安全多方θ-连接算法
引用本文:张媛媛,李书缘,史烨轩,周南,徐毅,许可.面向数据联邦的安全多方θ-连接算法[J].软件学报,2023,34(3):1109-1125.
作者姓名:张媛媛  李书缘  史烨轩  周南  徐毅  许可
作者单位:软件开发环境国家重点实验室(北京航空航天大学), 北京 100191;北京航空航天大学 计算机学院, 北京 100191;软件开发环境国家重点实验室(北京航空航天大学), 北京 100191;北京航空航天大学 计算机学院, 北京 100191;北京航空航天大学 沈元荣誉学院, 北京 100191;软件开发环境国家重点实验室(北京航空航天大学), 北京 100191;未来区块链与隐私计算高精尖创新中心(北京航空航天大学), 北京 100191;北京航空航天大学 人工智能研究院, 北京 100191;软件开发环境国家重点实验室(北京航空航天大学), 北京 100191;未来区块链与隐私计算高精尖创新中心(北京航空航天大学), 北京 100191;北京航空航天大学 计算机学院, 北京 100191
基金项目:国家重点研发计划(2018AAA0101100);国家自然科学基金(U1811463,62076017);软件开发环境国家重点实验室(北京航空航天大学)开放课题(SKLSDE-2020ZX-07)
摘    要:近年来,多个国家地区出台了一系列数据安全相关的法律,例如欧盟的《通用数据保护条例》等.这些相关法律法规的出台,加剧了各企业机构等多方之间数据共享难的数据孤岛问题.数据联邦(data federation)正是解决该问题的可能出路.数据联邦是指多个数据拥有方在不泄露各自原始数据的前提下,结合安全多方计算等隐私计算技术,联合完成查询任务的计算.这一概念已成为近年来的研究热点,并涌现出一系列相关的代表性系统工作,如SMCQL、Conclave.然而,针对关系数据库系统中核心的连接查询,现有数据联邦系统还存在如下问题:首先,连接种类单一,难以满足复杂连接条件下的查询需求;其次,算法性能低下,由于现有系统往往直接调用安全工具库,其运行时间与通信开销高昂.因此,针对以上问题进行研究,提出了数据联邦下连接算法.主要贡献如下:首先,设计实现了面向多方的联邦安全算子,能够支持多种运算;其次,提出了支持θ-连接的联邦连接算法与优化策略,显著减少了连接查询所需安全计算代价;最后,基于基准数据集TPC-H,验证了该算法的性能.实验结果表明,与现有数据联邦系统SMCQL、Conclave相比,该算法能够将运行时...

关 键 词:数据联邦  连接查询  安全多方计算
收稿时间:2022/5/16 0:00:00
修稿时间:2022/9/7 0:00:00

Secure Multi-part-Join Algorithms over Data Federation
ZHANG Yuan-Yuan,LI Shu-Yuan,SHI Ye-Xuan,ZHOU Nan,XU Yi,XU Ke.Secure Multi-part-Join Algorithms over Data Federation[J].Journal of Software,2023,34(3):1109-1125.
Authors:ZHANG Yuan-Yuan  LI Shu-Yuan  SHI Ye-Xuan  ZHOU Nan  XU Yi  XU Ke
Affiliation:State Key Laboratory of Software Development Enviroment (Beihang University), Beijing 100191, China;School of Computer Science and Engineering, Beihang University, Beijing 100191, China;State Key Laboratory of Software Development Enviroment (Beihang University), Beijing 100191, China;School of Computer Science and Engineering, Beihang University, Beijing 100191, China;the Shenyuan Honors College, Beihang University, Beijing 100191, China;State Key Laboratory of Software Development Enviroment (Beihang University), Beijing 100191, China;Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing (Beihang University), Beijing 100191, China;Institute of Artificial Intelligence, Beihang University, Beijing 100191, China; State Key Laboratory of Software Development Enviroment (Beihang University), Beijing 100191, China;Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing (Beihang University), Beijing 100191, China;School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Abstract:Recently,many countries and regions have enacted data security policies,such as the General Data Protection Regulation proposed by the EU.The release of related laws and regulations has aggravated the problem of data silos,which makes it difficult to share data among various data owners.The Data Federation is a possible solution to this problem.Data federation refers to the calculation of query tasks jointly performed by multiple data owners without protecing their original data and combining privacy computing technologies such as secure multi-party computing.This concept has become a research trend in recent years,and a series of representative systems have been proposed such as SMCQL and Conclave.However,for the core relational database system join query,the existing data federation system still has the following problems.First of all,the join query type is single,it is difficult to meet the query requirements under complex join conditions.Secondly,the algorithm performance has huge improvement space,because the existing systems often call the security tool library directly,which has high running time and communication overhead.Therefore,we propose a data federation join algorithm to address the above issues.The main contributions of this paper are as follows.Firstly,we design and implement multiparty-oriented federation security operators,which can support a variety of operations.Secondly,we propose a federated theta-join algorithm and an optimization strategy to significantly reduce the security computation cost.Finally,we verify the performance of this paper based on the benchmark dataset TPC-H.The experimental results show that the proposed algorithm can reduce the runtime and communication overhead by 61.33% and 95.26% compared with the existing data federation system SMCQL and Conclave.
Keywords:data federation  join query  secure multi-party computation
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