Private set intersection(PSI) allows two parties to compute the intersection of their private sets while revealing nothing except the intersection. With the development of fog computing, the need has arisen to delegat...Private set intersection(PSI) allows two parties to compute the intersection of their private sets while revealing nothing except the intersection. With the development of fog computing, the need has arisen to delegate PSI on outsourced datasets to the fog. However, the existing PSI schemes are based on either fully homomorphic encryption(FHE) or pairing computation. To the best of our knowledge, FHE and pairing operations consume a huge amount of computational resource. It is therefore an untenable scenario for resource-limited clients to carry out these operations. Furthermore, these PSI schemes cannot be applied to fog computing due to some inherent problems such as unacceptable latency and lack of mobility support. To resolve this problem, we first propose a novel primitive called " faster fog-aided private set intersection with integrity preserving", where the fog conducts delegated intersection operations over encrypted data without the decryption capacity. One of our technical highlights is to reduce the computation cost greatly by eliminating the FHE and pairing computation. Then we present a concrete construction and prove its security required under some cryptographic assumptions. Finally, we make a detailed theoretical analysis and simulation, and compare the results with those of the state-of-the-art schemes in two respects: communication overhead and computation overhead. The theoretical analysis and simulation show that our scheme is more efficient and practical.展开更多
In modern society,it is necessary to perform some secure computations for private sets between different entities.For instance,two merchants desire to calculate the number of common customers and the total number of u...In modern society,it is necessary to perform some secure computations for private sets between different entities.For instance,two merchants desire to calculate the number of common customers and the total number of users without disclosing their own privacy.In order to solve the referred problem,a semi-quantum protocol for private computation of cardinalities of set based on Greenberger-Horne-Zeilinger(GHZ)states is proposed for the first time in this paper,where all the parties just perform single-particle measurement if necessary.With the assistance of semi-honest third party(TP),two semi-quantum participants can simultaneously obtain intersection cardinality and union cardinality.Furthermore,security analysis shows that the presented protocol can stand against some well-known quantum attacks,such as intercept measure resend attack,entangle measure attack.Compared with the existing quantum protocols of Private Set Intersection Cardinality(PSI-CA)and Private Set Union Cardinality(PSU-CA),the complicated oracle operations and powerful quantum capacities are not required in the proposed protocol.Therefore,it seems more appropriate to implement this protocol with current technology.展开更多
隐私集合交集(private set intersection,PSI)协议一直是解决用户隐私保护需求和合作共享需求间矛盾的有效工具.面对计算资源受限场景下的多方求交计算,本文提出了支持子集匹配且可验证的云辅助多方PSI协议(tag-based and verifiable cl...隐私集合交集(private set intersection,PSI)协议一直是解决用户隐私保护需求和合作共享需求间矛盾的有效工具.面对计算资源受限场景下的多方求交计算,本文提出了支持子集匹配且可验证的云辅助多方PSI协议(tag-based and verifiable cloud-assisted multi-party PSI,TVC-MPSI).首先,TVC-MPSI应用星型网络拓扑结构,增加对单个云服务器的安全要求,仅利用密文交集基数和交集的多项式形式确保了交集的可验证性;其次,当客户端的集合包含多个子集时,引入了Pedersen门限可验证的秘密共享技术来实现对集合子集的匹配,从而实现细粒度的交集运算;除此之外,引入基于RSA的局部可验证签名算法(local verifiable aggregate signatures,LVS),保证云服务器端和客户端身份的不可伪造性;最后,通过正确性和安全性分析,以及全面的性能对比,表明协议在保证安全性的同时拥有较好的性能.展开更多
多方隐私集合交集(multiparty private set intersection,MPSI)作为安全计算领域一种保护数据安全的计算技术,支持在不泄露任何参与方隐私的前提下,计算多个参与方数据集的交集,可通过同态加密、不经意传输等技术手段实现.但现有基于同...多方隐私集合交集(multiparty private set intersection,MPSI)作为安全计算领域一种保护数据安全的计算技术,支持在不泄露任何参与方隐私的前提下,计算多个参与方数据集的交集,可通过同态加密、不经意传输等技术手段实现.但现有基于同态加密的MPSI协议存在计算效率低、交互轮数多等问题,且通过交互无法实现交集用户保密数据的计算.为此,首先基于布隆过滤器和ElGamal算法提出了n方交集用户的秘密信誉值比较协议.进一步针对查询交集失败的问题,基于信誉值过滤器和多密钥加解密,提出用户交集基数协议并完成多方秘密信誉值评估.实验结果表明,研究提出的2种协议满足半诚实安全,可抵抗n-1个参与方的合谋且执行时间优于其他方案.展开更多
隐私集合交集基数(private set intersection cardinality,PSI-CA)协议允许各参与方仅获知交集大小而不暴露其他信息.以测量广告转换率为例,广告平台的广告浏览者数量远少于服务提供商的服务订阅者数量,且服务提供商的用户集合不断变化...隐私集合交集基数(private set intersection cardinality,PSI-CA)协议允许各参与方仅获知交集大小而不暴露其他信息.以测量广告转换率为例,广告平台的广告浏览者数量远少于服务提供商的服务订阅者数量,且服务提供商的用户集合不断变化.然而,大多数现有PSI-CA协议不支持集合的动态更新.为此,提出了一种基于交换加密和动态布隆过滤器的PSI-CA协议,适用于非平衡场景,并支持集合动态更新.安全性证明表明,该协议在随机谕言机模型下是可证明安全的;性能分析和仿真实验结果表明,该协议能够以可接受的开销实现交集基数的计算,且动态布隆过滤器的误判率控制在较低水平.展开更多
随着物联网和大数据技术的发展,在计算机和手机上出现了大量分布式应用程序.然而现有的分布式数据处理方式已不能很好地满足用户对隐私保护的需求.隐私集合交集(private set intersection,PSI)协议作为一项典型的面向隐私保护的分布式...随着物联网和大数据技术的发展,在计算机和手机上出现了大量分布式应用程序.然而现有的分布式数据处理方式已不能很好地满足用户对隐私保护的需求.隐私集合交集(private set intersection,PSI)协议作为一项典型的面向隐私保护的分布式集合计算技术,允许各参与方输入其私有集合,共同计算集合的交集,且不泄露除交集以外的任何信息.PSI协议作为安全多方计算的一种重要应用,已被广泛应用于隐私计算领域,具有重要的理论和实践意义.首先介绍PSI协议的基本密码技术、敌手模型、安全证明、编程框架等基础知识;其次系统总结了构造传统PSI协议的设计框架:基于公钥加密体制的框架、基于混淆电路的框架、基于不经意传输的框架;随后介绍PSI协议核心的隐私集合元素比较技术工具:不经意伪随机函数、不经意多项式评估、布隆过滤器等;进一步地详细阐述了适应新型应用场景的PSI方案:基于云辅助的PSI、非平衡型PSI、基于阈值的PSI和多方PSI;最后总结并展望面向隐私保护的集合交集计算中亟待解决问题和发展方向.展开更多
隐私集合交集(private set intersection,PSI)是隐私计算中的热点,其允许参与两方在不泄露任何额外信息的要求下计算交集.现有的隐私集合交集计算方案对参与双方的计算能力要求高,且计算能力差的参与方无法在保证集合数据隐私的前提下...隐私集合交集(private set intersection,PSI)是隐私计算中的热点,其允许参与两方在不泄露任何额外信息的要求下计算交集.现有的隐私集合交集计算方案对参与双方的计算能力要求高,且计算能力差的参与方无法在保证集合数据隐私的前提下将计算安全外包给云服务器.设计了一种新的不经意两方分布式伪随机函数,允许半可信的云服务器参与相等性测试,又不泄露参与方任何集合信息.基于该不经意伪随机函数构建了半可信云服务器辅助的隐私集合交集计算协议,将主要计算量外包给云服务器.在半诚实模型下证明了协议的安全性.同时,该协议可保密地计算隐私集合交集的基数.通过与现有协议分析与实验性能比较,该协议效率高,计算复杂度与通信复杂度均与集合大小呈线性关系,适用于客户端设备受限的应用场景.展开更多
基金Project supported by the National Natural Science Foundation of China(Nos.61872069,61772127,61703088,and 61472184)
文摘Private set intersection(PSI) allows two parties to compute the intersection of their private sets while revealing nothing except the intersection. With the development of fog computing, the need has arisen to delegate PSI on outsourced datasets to the fog. However, the existing PSI schemes are based on either fully homomorphic encryption(FHE) or pairing computation. To the best of our knowledge, FHE and pairing operations consume a huge amount of computational resource. It is therefore an untenable scenario for resource-limited clients to carry out these operations. Furthermore, these PSI schemes cannot be applied to fog computing due to some inherent problems such as unacceptable latency and lack of mobility support. To resolve this problem, we first propose a novel primitive called " faster fog-aided private set intersection with integrity preserving", where the fog conducts delegated intersection operations over encrypted data without the decryption capacity. One of our technical highlights is to reduce the computation cost greatly by eliminating the FHE and pairing computation. Then we present a concrete construction and prove its security required under some cryptographic assumptions. Finally, we make a detailed theoretical analysis and simulation, and compare the results with those of the state-of-the-art schemes in two respects: communication overhead and computation overhead. The theoretical analysis and simulation show that our scheme is more efficient and practical.
基金supported by the National Natural Science Foundation of China(61802118)Natural Science Foundation of Heilongjiang Province(YQ2020F013)supported by the Advanced Programs of Heilongjiang Province for the Overseas Scholars and the Outstanding Youth Fund of Heilongjiang University and the Heilongjiang University Innovation Fund(YJSCX2022-247HLJU)
文摘In modern society,it is necessary to perform some secure computations for private sets between different entities.For instance,two merchants desire to calculate the number of common customers and the total number of users without disclosing their own privacy.In order to solve the referred problem,a semi-quantum protocol for private computation of cardinalities of set based on Greenberger-Horne-Zeilinger(GHZ)states is proposed for the first time in this paper,where all the parties just perform single-particle measurement if necessary.With the assistance of semi-honest third party(TP),two semi-quantum participants can simultaneously obtain intersection cardinality and union cardinality.Furthermore,security analysis shows that the presented protocol can stand against some well-known quantum attacks,such as intercept measure resend attack,entangle measure attack.Compared with the existing quantum protocols of Private Set Intersection Cardinality(PSI-CA)and Private Set Union Cardinality(PSU-CA),the complicated oracle operations and powerful quantum capacities are not required in the proposed protocol.Therefore,it seems more appropriate to implement this protocol with current technology.
文摘多方隐私集合交集(multiparty private set intersection,MPSI)作为安全计算领域一种保护数据安全的计算技术,支持在不泄露任何参与方隐私的前提下,计算多个参与方数据集的交集,可通过同态加密、不经意传输等技术手段实现.但现有基于同态加密的MPSI协议存在计算效率低、交互轮数多等问题,且通过交互无法实现交集用户保密数据的计算.为此,首先基于布隆过滤器和ElGamal算法提出了n方交集用户的秘密信誉值比较协议.进一步针对查询交集失败的问题,基于信誉值过滤器和多密钥加解密,提出用户交集基数协议并完成多方秘密信誉值评估.实验结果表明,研究提出的2种协议满足半诚实安全,可抵抗n-1个参与方的合谋且执行时间优于其他方案.
文摘隐私集合交集基数(private set intersection cardinality,PSI-CA)协议允许各参与方仅获知交集大小而不暴露其他信息.以测量广告转换率为例,广告平台的广告浏览者数量远少于服务提供商的服务订阅者数量,且服务提供商的用户集合不断变化.然而,大多数现有PSI-CA协议不支持集合的动态更新.为此,提出了一种基于交换加密和动态布隆过滤器的PSI-CA协议,适用于非平衡场景,并支持集合动态更新.安全性证明表明,该协议在随机谕言机模型下是可证明安全的;性能分析和仿真实验结果表明,该协议能够以可接受的开销实现交集基数的计算,且动态布隆过滤器的误判率控制在较低水平.
文摘随着物联网和大数据技术的发展,在计算机和手机上出现了大量分布式应用程序.然而现有的分布式数据处理方式已不能很好地满足用户对隐私保护的需求.隐私集合交集(private set intersection,PSI)协议作为一项典型的面向隐私保护的分布式集合计算技术,允许各参与方输入其私有集合,共同计算集合的交集,且不泄露除交集以外的任何信息.PSI协议作为安全多方计算的一种重要应用,已被广泛应用于隐私计算领域,具有重要的理论和实践意义.首先介绍PSI协议的基本密码技术、敌手模型、安全证明、编程框架等基础知识;其次系统总结了构造传统PSI协议的设计框架:基于公钥加密体制的框架、基于混淆电路的框架、基于不经意传输的框架;随后介绍PSI协议核心的隐私集合元素比较技术工具:不经意伪随机函数、不经意多项式评估、布隆过滤器等;进一步地详细阐述了适应新型应用场景的PSI方案:基于云辅助的PSI、非平衡型PSI、基于阈值的PSI和多方PSI;最后总结并展望面向隐私保护的集合交集计算中亟待解决问题和发展方向.
文摘隐私集合交集(private set intersection,PSI)是隐私计算中的热点,其允许参与两方在不泄露任何额外信息的要求下计算交集.现有的隐私集合交集计算方案对参与双方的计算能力要求高,且计算能力差的参与方无法在保证集合数据隐私的前提下将计算安全外包给云服务器.设计了一种新的不经意两方分布式伪随机函数,允许半可信的云服务器参与相等性测试,又不泄露参与方任何集合信息.基于该不经意伪随机函数构建了半可信云服务器辅助的隐私集合交集计算协议,将主要计算量外包给云服务器.在半诚实模型下证明了协议的安全性.同时,该协议可保密地计算隐私集合交集的基数.通过与现有协议分析与实验性能比较,该协议效率高,计算复杂度与通信复杂度均与集合大小呈线性关系,适用于客户端设备受限的应用场景.
