{"title":"Labeled Private Set Intersection From Distributed Point Function","authors":"Qi Liu;Xiaojie Guo;Kang Yang;Yu Yu","doi":"10.1109/TIFS.2025.3550059","DOIUrl":null,"url":null,"abstract":"Private Set Intersection (PSI) allows two mutually distrusting parties to compute the intersection of their sets without revealing any additional information, and has found numerous applications. A part of applications require labeled PSI in the unbalanced setting, where a server holds a label for each item in a set that is much larger than the set held by a client, and the client obtains the intersection and the corresponding labels. In this paper, we present a new concretely efficient labeled PSI protocol in the unbalanced setting, without using computation-heavy homomorphic encryption. Our protocol is based on Distributed Point Function (DPF) with hardware acceleration from fixed-key AES-NI, and has communication complexity linear in the size of a small set of the client and sublinear in the size of a large set of the server. Our protocol exploits two Oblivious Pesudorandom Function (OPRF) protocols, based on Diffle-Hellman PRFs or block ciphers, to achieve a trade-off between computation and communication. Our implementation demonstrates that our protocol outperforms the previous labeled and unbalanced PSI protocols. In particular, for two sets with respective <inline-formula> <tex-math>$2^{24}$ </tex-math></inline-formula> and 1 items, where each item has a 32-byte label, our protocol takes 1.19 seconds for an end-to-end performance, resulting in <inline-formula> <tex-math>$26 \\times $ </tex-math></inline-formula> improvement compared to the state-of-the-art protocol by Cong et al. (CCS 2021). In terms of the cost of the one-time initialization, we speed up the computations more than <inline-formula> <tex-math>$325\\times $ </tex-math></inline-formula> in the above comparison.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2970-2983"},"PeriodicalIF":6.3000,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Information Forensics and Security","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10919108/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Private Set Intersection (PSI) allows two mutually distrusting parties to compute the intersection of their sets without revealing any additional information, and has found numerous applications. A part of applications require labeled PSI in the unbalanced setting, where a server holds a label for each item in a set that is much larger than the set held by a client, and the client obtains the intersection and the corresponding labels. In this paper, we present a new concretely efficient labeled PSI protocol in the unbalanced setting, without using computation-heavy homomorphic encryption. Our protocol is based on Distributed Point Function (DPF) with hardware acceleration from fixed-key AES-NI, and has communication complexity linear in the size of a small set of the client and sublinear in the size of a large set of the server. Our protocol exploits two Oblivious Pesudorandom Function (OPRF) protocols, based on Diffle-Hellman PRFs or block ciphers, to achieve a trade-off between computation and communication. Our implementation demonstrates that our protocol outperforms the previous labeled and unbalanced PSI protocols. In particular, for two sets with respective $2^{24}$ and 1 items, where each item has a 32-byte label, our protocol takes 1.19 seconds for an end-to-end performance, resulting in $26 \times $ improvement compared to the state-of-the-art protocol by Cong et al. (CCS 2021). In terms of the cost of the one-time initialization, we speed up the computations more than $325\times $ in the above comparison.
期刊介绍:
The IEEE Transactions on Information Forensics and Security covers the sciences, technologies, and applications relating to information forensics, information security, biometrics, surveillance and systems applications that incorporate these features