To address the problem that existing bipartite secret sharing scheme is short of dynamic characteristic, and to solve the problem that each participant can only use secret share once, this paper proposed a bipartite (...To address the problem that existing bipartite secret sharing scheme is short of dynamic characteristic, and to solve the problem that each participant can only use secret share once, this paper proposed a bipartite (n1+n2, m1+m2)-threshold multi-secret sharing scheme which combined cryptography and hypersphere geometry. In this scheme, we introduced a bivariate function and a coordinate function over finite field Zp to calculate the derived points of secret share, which can reconstruct the shared secrets by producing the intersection point of hypernormal plane and normal line on the hypertangent plane. At the initial stage the secret dealer distributes to each participant a secret share that can be kept secret based on the intractability of discrete logarithm problem and need not be changed with updating the shared secrets.Each cooperative participant only needs to submit a derived point calculated from the secret share without exposing this secret share during the process of reconstructing the shared secret. Analyses indicate that the proposed scheme is not only sound and secure because of hypersphere geometric properties and the difficulty of discrete logarithm problem, but also efficient because of its well dynamic behavior and the invariant secret share. Therefore, this bipartite threshold multi-secret sharing scheme is easy to implement and is applicable in practical settings.展开更多
A threshold scheme, which is introduced by Shamir in 1979, is very famous as a secret sharing scheme. We can consider that this scheme is based on Lagrange＇s interpolation formula. A secret sharing scheme has one key...A threshold scheme, which is introduced by Shamir in 1979, is very famous as a secret sharing scheme. We can consider that this scheme is based on Lagrange＇s interpolation formula. A secret sharing scheme has one key. On the other hand, a multi-secret sharing scheme has more than one key, that is, a multi-secret sharing scheme has p （〉_ 2） keys. Dealer distribute shares of keys among n participants. Gathering t （〈 n） participants, keys can be reconstructed. Yang et al. （2004） gave a scheme of a （t, n） multi-secret sharing based on Lagrange＇s interpolation. Zhao et al. （2007） gave a scheme of a （t, n） verifiable multi-secret sharing based on Lagrange＇s interpolation. Recently, Adachi and Okazaki give a scheme of a （t, n） multi-secret sharing based on Hermite interpolation, in the case ofp 〈 t. In this paper, we give a scheme ofa （t, n） verifiable multi-secret sharing based on Hermite interpolation.展开更多
A secret sharing scheme is one of cryptographies. A threshold scheme, which is introduced by Shamir in 1979, is very famous as a secret sharing scheme. We can consider that this scheme is based on Lagrange's inter...A secret sharing scheme is one of cryptographies. A threshold scheme, which is introduced by Shamir in 1979, is very famous as a secret sharing scheme. We can consider that this scheme is based on Lagrange's interpolation formula. A secret sharing scheme has one key. On the other hand, a multi-secret sharing scheme has more than one keys;that is, a multi-secret sharing scheme has p (≥2) keys. Dealers distribute shares of keys among n participants. Gathering t (≤n) participants, keys can be reconstructed. In this paper, we give a scheme of a (t,n) multi-secret sharing based on Hermite interpolation, in the case of p≤t.展开更多
In this paper the linear multi-secret sharing schemes are studied by using monotone span programs. A relation between computing monotone Boolean functions by using monotone span programs and realizing multi-access str...In this paper the linear multi-secret sharing schemes are studied by using monotone span programs. A relation between computing monotone Boolean functions by using monotone span programs and realizing multi-access structures by using linear multi-secret sharing schemes is shown. Furthermore, the concept of optimal linear multi-secret sharing scheme is presented and the several schemes are proved to be optimal.展开更多
In a linear multi-secret sharing scheme with non-threshold structures, several secret values are shared among n participants, and every secret value has a specified access structure. The efficiency of a multi- secret ...In a linear multi-secret sharing scheme with non-threshold structures, several secret values are shared among n participants, and every secret value has a specified access structure. The efficiency of a multi- secret sharing scheme is measured by means of the complexity a and the randomness . Informally, the com- plexity a is the ratio between the maximum of information received by each participant and the minimum of information corresponding to every key. The randomness is the ratio between the amount of information distributed to the set of users U = {1, …, n} and the minimum of information corresponding to every key. In this paper, we discuss a and of any linear multi-secret sharing schemes realized by linear codes with non-threshold structures, and provide two algorithms to make a and to be the minimum, respectively. That is, they are optimal.展开更多
随着基于位置服务(LBS)的普及和发展,用户位置数据的隐私保护已成为一个紧迫的问题。针对现有的基于匿名的隐私保护仍面临匿名位置和查询内容中的语义信息对匿名安全性问题的挑战,基于可验证秘密共享算法提出一种多匿名器架构的双重隐...随着基于位置服务(LBS)的普及和发展,用户位置数据的隐私保护已成为一个紧迫的问题。针对现有的基于匿名的隐私保护仍面临匿名位置和查询内容中的语义信息对匿名安全性问题的挑战,基于可验证秘密共享算法提出一种多匿名器架构的双重隐私保护算法(Dual Privacy Protection Algorithm based on Multi-Anonymizer Architecture,MAADPPA)。与现有算法不同,该算法通过集成位置匿名和查询加密来增强位置隐私保护。首先,提出可验证秘密共享和多匿名器的查询加密算法,以增强查询安全性。其次,设计一种匿名方法,通过满足个人语义多样性的匿名位置和替换敏感语义位置来增强语义位置隐私。实验结果表明,MAA-DPPA隐私性最高,MAA-DPPA在多个条件下相对于基于道路网络语义多样性的匿名集构造的算法的平均提高百分比范围在38.3%~59.8%,且相对于基于语义和权衡感知的提高范围在51.1%~76.5%,MAA-DPPA算法在提高隐私保护性的同时显著提升了算法效率,验证了其在用户位置隐私保护中的有效性。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
随着应用场景的多样化和私有数据共享规模的扩大,多方隐私集合交集计算(private set intersection,PSI)成为协同数据处理中的一个研究热点。然而,现有的多方PSI协议大多存在着参与方之间开销不平衡的问题,这不仅影响参与方之间的公平性...随着应用场景的多样化和私有数据共享规模的扩大,多方隐私集合交集计算(private set intersection,PSI)成为协同数据处理中的一个研究热点。然而,现有的多方PSI协议大多存在着参与方之间开销不平衡的问题,这不仅影响参与方之间的公平性,还影响协议的总体效率。针对该问题,基于不经意键值存储和秘密分享技术,提出了一个高效、平衡的多方PSI协议(EBMPSI)。该协议具备面向所有半诚实敌手的安全性,且可抵抗多个参与方的合谋攻击。理论和实验分析表明,EBMPSI协议有效地平衡了各个参与方之间的计算和通信开销。与现有方案的实验对比表明,EBMPSI协议在资源分布均匀的环境中展现出更高的执行效率。展开更多
聚类是一种流行的无监督机器学习技术,它将相似的数据分组成簇。聚类被应用于众多领域的数据分析,包括金融分析、医疗分析等。许多聚类应用都包含了敏感信息,由于数据隐私保护政策,这些敏感信息在聚类时不应被泄露。随着数据分析技术的...聚类是一种流行的无监督机器学习技术,它将相似的数据分组成簇。聚类被应用于众多领域的数据分析,包括金融分析、医疗分析等。许多聚类应用都包含了敏感信息,由于数据隐私保护政策,这些敏感信息在聚类时不应被泄露。随着数据分析技术的发展,现在通常需要对多个来源的数据进行聚类,以提高分析的质量,这需要高效的隐私保护聚类算法来保护各个参与方的隐私。然而,现有的隐私保护聚类方法仅支持2~3个参与方共同聚类。本文实现了联邦密度聚类算法(Federated Density-Based Spatial Clustering of Applications with Noise,FDBSCAN)。FDBSCAN是一种基于多方安全计算技术的支持任意多参与方的高效隐私保护聚类方法。FDBSCAN算法基于DBSCAN算法对点的定义,构造了多个聚类簇,每个簇都由至少一个核心点和所有由它可达的点组成。在FDBSCAN中,各参与方利用秘密共享加密数据并分享给其他参与方进行联合聚类,避免了参与方隐私信息泄露与中间信息泄露问题。FDBSCAN还基于秘密共享和二进制共享的混合协议实现了隐私保护的条件控制语句。实验表明,与现有的隐私保护聚类算法相比,FDBSCAN能够支持更多的参与方,并且在聚类准确度与效率上表现更佳。在常见的聚类场景中,FDBSCAN能够获得与明文DBSCAN算法相同的聚类准确度,并且在计算效率上达到了可用水平。FDBSCAN算法在双方联合聚类的场景中,相较于已有的隐私保护聚类算法,在真实环境数据集中表现出了更高的效率。本文还针对轨迹聚类这一应用场景实现了联邦轨迹聚类算法(Federated Trajectory Clustering,FTC)。FTC使用FDBSCAN进行聚类,并提出了高效隐私安全的轨迹距离计算方法,获得了较好的轨迹聚类效果。实验结果表明,FTC算法在轮廓系数等指标中的表现要优于现有的轨迹聚类算法。展开更多
文摘To address the problem that existing bipartite secret sharing scheme is short of dynamic characteristic, and to solve the problem that each participant can only use secret share once, this paper proposed a bipartite (n1+n2, m1+m2)-threshold multi-secret sharing scheme which combined cryptography and hypersphere geometry. In this scheme, we introduced a bivariate function and a coordinate function over finite field Zp to calculate the derived points of secret share, which can reconstruct the shared secrets by producing the intersection point of hypernormal plane and normal line on the hypertangent plane. At the initial stage the secret dealer distributes to each participant a secret share that can be kept secret based on the intractability of discrete logarithm problem and need not be changed with updating the shared secrets.Each cooperative participant only needs to submit a derived point calculated from the secret share without exposing this secret share during the process of reconstructing the shared secret. Analyses indicate that the proposed scheme is not only sound and secure because of hypersphere geometric properties and the difficulty of discrete logarithm problem, but also efficient because of its well dynamic behavior and the invariant secret share. Therefore, this bipartite threshold multi-secret sharing scheme is easy to implement and is applicable in practical settings.
文摘A threshold scheme, which is introduced by Shamir in 1979, is very famous as a secret sharing scheme. We can consider that this scheme is based on Lagrange＇s interpolation formula. A secret sharing scheme has one key. On the other hand, a multi-secret sharing scheme has more than one key, that is, a multi-secret sharing scheme has p （〉_ 2） keys. Dealer distribute shares of keys among n participants. Gathering t （〈 n） participants, keys can be reconstructed. Yang et al. （2004） gave a scheme of a （t, n） multi-secret sharing based on Lagrange＇s interpolation. Zhao et al. （2007） gave a scheme of a （t, n） verifiable multi-secret sharing based on Lagrange＇s interpolation. Recently, Adachi and Okazaki give a scheme of a （t, n） multi-secret sharing based on Hermite interpolation, in the case ofp 〈 t. In this paper, we give a scheme ofa （t, n） verifiable multi-secret sharing based on Hermite interpolation.
文摘A secret sharing scheme is one of cryptographies. A threshold scheme, which is introduced by Shamir in 1979, is very famous as a secret sharing scheme. We can consider that this scheme is based on Lagrange's interpolation formula. A secret sharing scheme has one key. On the other hand, a multi-secret sharing scheme has more than one keys;that is, a multi-secret sharing scheme has p (≥2) keys. Dealers distribute shares of keys among n participants. Gathering t (≤n) participants, keys can be reconstructed. In this paper, we give a scheme of a (t,n) multi-secret sharing based on Hermite interpolation, in the case of p≤t.
基金supported by the National Natural Science Foundation of China(Grant Nos.60083002,90304012,2004CB318000).
文摘In this paper the linear multi-secret sharing schemes are studied by using monotone span programs. A relation between computing monotone Boolean functions by using monotone span programs and realizing multi-access structures by using linear multi-secret sharing schemes is shown. Furthermore, the concept of optimal linear multi-secret sharing scheme is presented and the several schemes are proved to be optimal.
基金Supported in part by the National Natural Science Foundation of China under Grant No.11271003the National Research Foundation for the Doctoral Program of Higher Education of China under Grant No.20134410110003+3 种基金High Level Talents Project of GuangdongGuangdong Provincial Natural Science Foundation under Grant No.S2012010009950the Project of Department of Education of Guangdong Province under Grant No 2013KJCX0146the Natural Science Foundation of Bureau of Education of Guangzhou under Grant No.2012A004
文摘In a linear multi-secret sharing scheme with non-threshold structures, several secret values are shared among n participants, and every secret value has a specified access structure. The efficiency of a multi- secret sharing scheme is measured by means of the complexity a and the randomness . Informally, the com- plexity a is the ratio between the maximum of information received by each participant and the minimum of information corresponding to every key. The randomness is the ratio between the amount of information distributed to the set of users U = {1, …, n} and the minimum of information corresponding to every key. In this paper, we discuss a and of any linear multi-secret sharing schemes realized by linear codes with non-threshold structures, and provide two algorithms to make a and to be the minimum, respectively. That is, they are optimal.
文摘随着基于位置服务(LBS)的普及和发展,用户位置数据的隐私保护已成为一个紧迫的问题。针对现有的基于匿名的隐私保护仍面临匿名位置和查询内容中的语义信息对匿名安全性问题的挑战,基于可验证秘密共享算法提出一种多匿名器架构的双重隐私保护算法(Dual Privacy Protection Algorithm based on Multi-Anonymizer Architecture,MAADPPA)。与现有算法不同,该算法通过集成位置匿名和查询加密来增强位置隐私保护。首先,提出可验证秘密共享和多匿名器的查询加密算法,以增强查询安全性。其次,设计一种匿名方法,通过满足个人语义多样性的匿名位置和替换敏感语义位置来增强语义位置隐私。实验结果表明,MAA-DPPA隐私性最高,MAA-DPPA在多个条件下相对于基于道路网络语义多样性的匿名集构造的算法的平均提高百分比范围在38.3%~59.8%,且相对于基于语义和权衡感知的提高范围在51.1%~76.5%,MAA-DPPA算法在提高隐私保护性的同时显著提升了算法效率,验证了其在用户位置隐私保护中的有效性。
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.
文摘随着应用场景的多样化和私有数据共享规模的扩大,多方隐私集合交集计算(private set intersection,PSI)成为协同数据处理中的一个研究热点。然而,现有的多方PSI协议大多存在着参与方之间开销不平衡的问题,这不仅影响参与方之间的公平性,还影响协议的总体效率。针对该问题,基于不经意键值存储和秘密分享技术,提出了一个高效、平衡的多方PSI协议(EBMPSI)。该协议具备面向所有半诚实敌手的安全性,且可抵抗多个参与方的合谋攻击。理论和实验分析表明,EBMPSI协议有效地平衡了各个参与方之间的计算和通信开销。与现有方案的实验对比表明,EBMPSI协议在资源分布均匀的环境中展现出更高的执行效率。
文摘聚类是一种流行的无监督机器学习技术,它将相似的数据分组成簇。聚类被应用于众多领域的数据分析,包括金融分析、医疗分析等。许多聚类应用都包含了敏感信息,由于数据隐私保护政策,这些敏感信息在聚类时不应被泄露。随着数据分析技术的发展,现在通常需要对多个来源的数据进行聚类,以提高分析的质量,这需要高效的隐私保护聚类算法来保护各个参与方的隐私。然而,现有的隐私保护聚类方法仅支持2~3个参与方共同聚类。本文实现了联邦密度聚类算法(Federated Density-Based Spatial Clustering of Applications with Noise,FDBSCAN)。FDBSCAN是一种基于多方安全计算技术的支持任意多参与方的高效隐私保护聚类方法。FDBSCAN算法基于DBSCAN算法对点的定义,构造了多个聚类簇,每个簇都由至少一个核心点和所有由它可达的点组成。在FDBSCAN中,各参与方利用秘密共享加密数据并分享给其他参与方进行联合聚类,避免了参与方隐私信息泄露与中间信息泄露问题。FDBSCAN还基于秘密共享和二进制共享的混合协议实现了隐私保护的条件控制语句。实验表明,与现有的隐私保护聚类算法相比,FDBSCAN能够支持更多的参与方,并且在聚类准确度与效率上表现更佳。在常见的聚类场景中,FDBSCAN能够获得与明文DBSCAN算法相同的聚类准确度,并且在计算效率上达到了可用水平。FDBSCAN算法在双方联合聚类的场景中,相较于已有的隐私保护聚类算法,在真实环境数据集中表现出了更高的效率。本文还针对轨迹聚类这一应用场景实现了联邦轨迹聚类算法(Federated Trajectory Clustering,FTC)。FTC使用FDBSCAN进行聚类,并提出了高效隐私安全的轨迹距离计算方法,获得了较好的轨迹聚类效果。实验结果表明,FTC算法在轮廓系数等指标中的表现要优于现有的轨迹聚类算法。
