{"title":"Delegated multi-party private set intersections from extendable output functions.","authors":"Aslı Bay","doi":"10.7717/peerj-cs.3141","DOIUrl":null,"url":null,"abstract":"<p><p>Operations on sensitive datasets from different parties are essential for various practical applications, such as verifying shopping lists or enforcing no-fly lists. Traditional methods often require one party to access both datasets, which poses privacy concerns. Private set operations provide a solution by enabling these functions without revealing the data involved. However, protocols involving three or more parties are generally much slower than unsecured methods. Outsourced private set operations, where computations are delegated to a non-colluding server, can significantly improve performance, though current protocols have not fully leveraged this assumption. We propose a new protocol that removes the need for public-key cryptography. Our non-interactive set intersection protocol relies solely on the security of an extendable output function, achieving high efficiency. Even in a ten-client setting with 16,384-element sets, the intersection can be computed in under 54 s without communication overhead. Our results indicate that substantial performance improvements can be made without sacrificing privacy, presenting a practical and efficient approach to private set operations.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3141"},"PeriodicalIF":2.5000,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453744/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"PeerJ Computer Science","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.7717/peerj-cs.3141","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Operations on sensitive datasets from different parties are essential for various practical applications, such as verifying shopping lists or enforcing no-fly lists. Traditional methods often require one party to access both datasets, which poses privacy concerns. Private set operations provide a solution by enabling these functions without revealing the data involved. However, protocols involving three or more parties are generally much slower than unsecured methods. Outsourced private set operations, where computations are delegated to a non-colluding server, can significantly improve performance, though current protocols have not fully leveraged this assumption. We propose a new protocol that removes the need for public-key cryptography. Our non-interactive set intersection protocol relies solely on the security of an extendable output function, achieving high efficiency. Even in a ten-client setting with 16,384-element sets, the intersection can be computed in under 54 s without communication overhead. Our results indicate that substantial performance improvements can be made without sacrificing privacy, presenting a practical and efficient approach to private set operations.
期刊介绍:
PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.