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
分布式计算有很多应用需要参与各方协同执行集合的一些计算但不泄露各自数据集的信息.保密集合交集(private set intersection,PSI)计算已经成为数据匹配、数据挖掘、推荐系统等应用中保护用户隐私的一个重要工具.本文的主要工作是构造...分布式计算有很多应用需要参与各方协同执行集合的一些计算但不泄露各自数据集的信息.保密集合交集(private set intersection,PSI)计算已经成为数据匹配、数据挖掘、推荐系统等应用中保护用户隐私的一个重要工具.本文的主要工作是构造无匹配差错的安全两方保密集合交集运算协议.着重探讨三个问题:(1)开发构造无匹配差错的两方保密集合交集计算所需要的工具(①面向有理数且具有语义安全性的加密方案,②便于集合匹配计算的称之为集合的定长向量编码方法);(2)无匹配差错的两方保密集合交集计算问题;(3)元素为有理数的保密集合交集计算问题.首先在标准模型下设计了一个能够加密有理数的方案,并证明了该方案能抗自适应性地选择明文攻击;而后又提出了一种便于集合匹配计算的,称之为集合的定长向量编码方法;最后基于有理数加密方案和集合的定长向量编码方法构造了两个面向有理数的、无匹配差错的两方保密集合交集协议.与先前的两方保密集合交集协议相较之,这两个协议不仅解决了无匹配差错的两方保密集合交集计算,还拓展了保密集合交集问题中隐私保护的范畴:除了可以保护各参与方的隐私数据外,还可以保护各参与方隐私数据的数量.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
多源异构数据融合的痛点在于数据的低价值密度性和分散性。数据的多源异构性增加了数据聚合的难度,导致数据价值极度零散,使得数据融合方法面对多源异构大数据无的放矢,无法有效关联零散价值的数据。隐私集合求交(Private Set Intersect...多源异构数据融合的痛点在于数据的低价值密度性和分散性。数据的多源异构性增加了数据聚合的难度,导致数据价值极度零散,使得数据融合方法面对多源异构大数据无的放矢,无法有效关联零散价值的数据。隐私集合求交(Private Set Intersection,PSI)不但可以使数据方放心提供数据,还可以将多源异构数据价值有效融合,是挖掘有效数据开展数据融合工作的新工具。为此,文章针对异构数据的整合、数据的多源以及大规模数据的并行处理3类问题,给出多源异构数据融合的3个新思路。展开更多
隐私集合求交(Private Set Intersection,PSI)属于隐私计算领域的特定应用问题,包括秘密共享、同态加密、不经意传输、混淆电路和Hash技术等基础知识,其兼具重要的理论意义与极强的现实应用价值。随着用户数据的隐私保护需求的日益提升,...隐私集合求交(Private Set Intersection,PSI)属于隐私计算领域的特定应用问题,包括秘密共享、同态加密、不经意传输、混淆电路和Hash技术等基础知识,其兼具重要的理论意义与极强的现实应用价值。随着用户数据的隐私保护需求的日益提升,PSI可以在满足依赖个人信息的业务的便利性的同时最大程度保护个人信息私密性需求。本文首先介绍了隐私集合求交的研究现状,其次按照底层密码技术对PSI进行分类并对比分析了它们的复杂度,对其优缺点进行对比分析,同时对比分析了基于不同密码技术的PSI的使用场景,最后指出其发展方向并得出结论。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.
文摘分布式计算有很多应用需要参与各方协同执行集合的一些计算但不泄露各自数据集的信息.保密集合交集(private set intersection,PSI)计算已经成为数据匹配、数据挖掘、推荐系统等应用中保护用户隐私的一个重要工具.本文的主要工作是构造无匹配差错的安全两方保密集合交集运算协议.着重探讨三个问题:(1)开发构造无匹配差错的两方保密集合交集计算所需要的工具(①面向有理数且具有语义安全性的加密方案,②便于集合匹配计算的称之为集合的定长向量编码方法);(2)无匹配差错的两方保密集合交集计算问题;(3)元素为有理数的保密集合交集计算问题.首先在标准模型下设计了一个能够加密有理数的方案,并证明了该方案能抗自适应性地选择明文攻击;而后又提出了一种便于集合匹配计算的,称之为集合的定长向量编码方法;最后基于有理数加密方案和集合的定长向量编码方法构造了两个面向有理数的、无匹配差错的两方保密集合交集协议.与先前的两方保密集合交集协议相较之,这两个协议不仅解决了无匹配差错的两方保密集合交集计算,还拓展了保密集合交集问题中隐私保护的范畴:除了可以保护各参与方的隐私数据外,还可以保护各参与方隐私数据的数量.
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.
文摘多源异构数据融合的痛点在于数据的低价值密度性和分散性。数据的多源异构性增加了数据聚合的难度,导致数据价值极度零散,使得数据融合方法面对多源异构大数据无的放矢,无法有效关联零散价值的数据。隐私集合求交(Private Set Intersection,PSI)不但可以使数据方放心提供数据,还可以将多源异构数据价值有效融合,是挖掘有效数据开展数据融合工作的新工具。为此,文章针对异构数据的整合、数据的多源以及大规模数据的并行处理3类问题,给出多源异构数据融合的3个新思路。
文摘隐私集合求交(Private Set Intersection,PSI)属于隐私计算领域的特定应用问题,包括秘密共享、同态加密、不经意传输、混淆电路和Hash技术等基础知识,其兼具重要的理论意义与极强的现实应用价值。随着用户数据的隐私保护需求的日益提升,PSI可以在满足依赖个人信息的业务的便利性的同时最大程度保护个人信息私密性需求。本文首先介绍了隐私集合求交的研究现状,其次按照底层密码技术对PSI进行分类并对比分析了它们的复杂度,对其优缺点进行对比分析,同时对比分析了基于不同密码技术的PSI的使用场景,最后指出其发展方向并得出结论。
