Zhuzhu Wang;Cui Hu;Bin Xiao;Yang Liu;Teng Li;Zhuo Ma;Jianfeng Ma
{"title":"垂直分区数据库上的外包隐私保护数据对齐","authors":"Zhuzhu Wang;Cui Hu;Bin Xiao;Yang Liu;Teng Li;Zhuo Ma;Jianfeng Ma","doi":"10.1109/TBDATA.2023.3284271","DOIUrl":null,"url":null,"abstract":"In the context of real-world secure outsourced computations, private data alignment has been always the essential preprocessing step. However, current private data alignment schemes, mainly circuit-based, suffer from high communication overhead and often need to transfer potentially gigabytes of data. In this paper, we propose a lightweight private data alignment protocol (called SC-PSI) that can overcome the bottleneck of communication. Specifically, SC-PSI involves four phases of computations, including data preprocessing, data outsourcing, private set member (PSM) evaluation and circuit computation (CC). Like prior works, the major overhead of SC-PSI mainly lies in the latter two phases. The improvement is SC-PSI utilizes the function secret sharing technique to develop the PSM protocol, which avoids the multiple rounds of communication to compute intersection set members. Moreover, benefited from our specially designed PSM protocol, SC-PSI does not to execute complex secure comparison circuits in the CC phase. Experimentally, we validate that compared to prior works, SC-PSI can save around 61.39% running time and 89.61% communication overhead.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"9 5","pages":"1408-1419"},"PeriodicalIF":7.5000,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Outsourced Privacy-Preserving Data Alignment on Vertically Partitioned Database\",\"authors\":\"Zhuzhu Wang;Cui Hu;Bin Xiao;Yang Liu;Teng Li;Zhuo Ma;Jianfeng Ma\",\"doi\":\"10.1109/TBDATA.2023.3284271\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the context of real-world secure outsourced computations, private data alignment has been always the essential preprocessing step. However, current private data alignment schemes, mainly circuit-based, suffer from high communication overhead and often need to transfer potentially gigabytes of data. In this paper, we propose a lightweight private data alignment protocol (called SC-PSI) that can overcome the bottleneck of communication. Specifically, SC-PSI involves four phases of computations, including data preprocessing, data outsourcing, private set member (PSM) evaluation and circuit computation (CC). Like prior works, the major overhead of SC-PSI mainly lies in the latter two phases. The improvement is SC-PSI utilizes the function secret sharing technique to develop the PSM protocol, which avoids the multiple rounds of communication to compute intersection set members. Moreover, benefited from our specially designed PSM protocol, SC-PSI does not to execute complex secure comparison circuits in the CC phase. Experimentally, we validate that compared to prior works, SC-PSI can save around 61.39% running time and 89.61% communication overhead.\",\"PeriodicalId\":13106,\"journal\":{\"name\":\"IEEE Transactions on Big Data\",\"volume\":\"9 5\",\"pages\":\"1408-1419\"},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2023-06-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Big Data\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10146512/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Big Data","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10146512/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Outsourced Privacy-Preserving Data Alignment on Vertically Partitioned Database
In the context of real-world secure outsourced computations, private data alignment has been always the essential preprocessing step. However, current private data alignment schemes, mainly circuit-based, suffer from high communication overhead and often need to transfer potentially gigabytes of data. In this paper, we propose a lightweight private data alignment protocol (called SC-PSI) that can overcome the bottleneck of communication. Specifically, SC-PSI involves four phases of computations, including data preprocessing, data outsourcing, private set member (PSM) evaluation and circuit computation (CC). Like prior works, the major overhead of SC-PSI mainly lies in the latter two phases. The improvement is SC-PSI utilizes the function secret sharing technique to develop the PSM protocol, which avoids the multiple rounds of communication to compute intersection set members. Moreover, benefited from our specially designed PSM protocol, SC-PSI does not to execute complex secure comparison circuits in the CC phase. Experimentally, we validate that compared to prior works, SC-PSI can save around 61.39% running time and 89.61% communication overhead.
期刊介绍:
The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.