Secure Two-Party Point-Circle Inclusion Problem

Privacy-preserving computational geometry is a special secure multi-party computation and has many applications. Previous protocols for determining whether a point is inside a circle are not secure enough. We present a two-round protocol for computing the distance between two private points and develop a more efficient protocol for the point-circle inclusion problem based on the distance protocol. In comparison with previous solutions, our protocol not only is more secure but also reduces the number of communication rounds and the number of modular multiplications significantly.     

一种字符串近似匹配的安全查询协议

数据库中字符串近似匹配查询不能完全保护查询双方的隐私信息。针对该问题,提出一种对数据库中字符串数据的近似匹配查询协议。采用安全计算编辑距离协议、同态加密、茫然传输等安全技术,在有效保护查询双方隐私信息的情况下,实现对字符串近似匹配的查询,并分析该协议的正确性、安全性及复杂性,结果表明,该方案是安全有效的。     

隐私保护DNA序列汉明距离计算问题

DNA序列承载着人体重要的生物学信息，如何在保护隐私的情况下正确地对不同的DNA序列进行比对，成为亟待研究的科学问题。汉明距离在一定程度上刻画了两个DNA序列的相似程度，在保护隐私的情况下，研究DNA序列的汉明距离计算问题。首先定义了DNA序列的0-1编码规则，该规则将长度为n的DNA序列编码成长度为4n的0-1串，证明了两个DNA序列的汉明距离等于它们的0-1编码串的汉明距离的一半。以此结论为基础，以GM加密算法为主要密码学工具，构造了计算DNA序列汉明距离的一个安全两方计算协议。在半诚实攻击者模型下，证明了协议的正确性，给出了基于模拟器的安全性证明，并对协议的效率进行了分析。     

保护隐私的曼哈顿距离计算及其推广应用

安全多方计算是信息时代保护隐私和信息安全的一项关键技术.安全多方科学计算是安全多方计算十分重要的组成部分,目前已经有许多安全多方科学计算问题的解决方案,但还有更多的问题值得人们去研究.关于曼哈顿距离的安全多方计算问题目前研究的结果很少,构造曼哈顿距离的安全计算协议在密码学中有着重要的理论意义,作为基础协议能够广泛应用于其他安全多方计算协议的构造,比如保密计算两点间路径问题,保密判定点与区间以及点与点集的关系问题,以及向量相似度的保密计算都可以归约到曼哈顿距离的安全多方计算问题.本文应用加密选择技巧与一种新的编码方法相结合,以Paillier加密算法为基础,对于不同的情形(无全集限制或有全集限制)设计两数之差绝对值的高效保密计算协议.并以此为基础,设计出两种不同情形下保密计算曼哈顿距离的协议.本文证明了在半诚实模型下这些协议是安全的,并通过模拟实验来测试协议的具体执行时间,理论分析和仿真结果表明本文方案是简单易行的.最后,文中给出实例阐明本文协议在理论以及实际中的广泛应用.     

Secure Hamming distance computation for biometrics using ideal-lattice and ring-LWE homomorphic encryption

With widespread development of biometrics, concerns about security and privacy are rapidly increasing. Homomorphic encryption enables us to operate on encrypted data without decryption, and it can be applied to construct a privacy-preserving biometric system. In this article, we apply two homomorphic encryption schemes based on ideal-lattice and ring-LWE (Learning with Errors), which both have homomorphic correctness over the ring of integers of a cyclotomic field. We compare the two schemes in applying them to privacy-preserving biometrics. In biometrics, the Hamming distance is used as a metric to compare two biometric feature vectors for authentication. We propose an efficient method for secure Hamming distance. Our method can pack a biometric feature vector into a single ciphertext, and it enables efficient computation of secure Hamming distance over our packed ciphertexts.     

基于同态加密算法的欧氏距离外包计算协议

针对外包存储数据在密文状态下有关欧氏距离无法计算的问题,构建了欧氏距离外包计算协议,降低了用户的计算负担,保护了数据隐私。回顾了分布式双陷门公钥密码方案。基于同态加密算法设计了安全的乘法协议、单个密钥加密下的完全平方式协议和联合公钥加密下的完全平方式协议,基于这三个基础计算协议设计了欧氏距离的外包计算协议。安全性分析表明该协议足够安全,效率分析显示该协议较为高效,并较好地解决了有关欧氏距离的外包计算问题,对于图像处理的发展有一定的促进作用。     

Privacy preserving record linkage in the presence of missing values

The problem of record linkage is to identify records from two datasets, which refer to the same entities (e.g. patients). A particular issue of record linkage is the presence of missing values in records, which has not been fully addressed. Another issue is how privacy and confidentiality can be preserved in the process of record linkage. In this paper, we propose an approach for privacy preserving record linkage in the presence of missing values. For any missing value in a record, our approach imputes the similarity measure between the missing value and the value of the corresponding field in any of the possible matching records from another dataset. We use the k-NNs (k Nearest Neighbours in the same dataset) of the record with the missing value and their distances to the record for similarity imputation. For privacy preservation, our approach uses the Bloom filter protocol in the settings of both standard privacy preserving record linkage without missing values and privacy preserving record linkage with missing values. We have conducted an experimental evaluation using three pairs of synthetic datasets with different rates of missing values. Our experimental results show the effectiveness and efficiency of our proposed approach.     

一个高效的安全两方近似模式匹配协议

近似模式匹配是模式匹配中最适合实际应用的变体之一,其功能是确定2个字符串之间的汉明距离是否小于某给定阈值.由于其实用性,近似模式匹配在人脸识别、基因匹配等方面具有广泛的应用.然而,由于私有数据的敏感性,数据拥有者往往不愿意共享其隐私数据.幸运的是,安全近似模式匹配可以在不泄露数据前提下完成匹配功能.首次基于茫然传输(oblivious transfer, OT)、同态加密(homomorphic encryption, HE)、茫然多项式计算(oblivious polynomial evaluation, OPE)以及隐私等值比较(private equality test, PEQT)技术提出了安全的、实用的近似模式匹配协议,并通过理想/现实模拟范式证明协议具有半诚实敌手安全性.就效率而言,与当前已有的安全近似模式匹配工作相比,协议在计算复杂度方面具有优势,将复杂度从O(nm)降为O(nτ),其中n为文本长度,m为模式长度,τ为给定阈值.最后,为了检验高效性,对协议进行了性能评估.实验结果表明：当模式长度为2\\+6且文本长度为2\\+\\{12\\}时,协议仅需要10 s运行时间.     

基于编辑距离的远程数据库安全搜索协议

远程数据库安全搜索作为安全多方计算的一项应用变得越来越重要,它被广泛地应用到电子商务领域中。给出了基于编辑距离的远程数据库安全搜索协议,回顾了编辑距离的定义及计算方法,利用安全多方计算的相关知识构建了一系列基础安全协议以实现双方安全计算编辑距离,并对这些协议的计算复杂度进行了分析。最后给出了基于编辑距离的远程数据库安全搜索协议和协议的代价,该协议可以用在基于DNA序列匹配的远程数据库安全搜索中。     

向量等分量数的保密计算及应用

随着信息技术的快速发展, 在保护数据隐私的条件下进行多方合作计算变得越来越普及, 安全多方计算已经成为解决这类保密计算问题的核心技术. 向量的保密计算是安全多方计算的重要研究方向, 目前有很多研究成果, 包括保密计算向量的点积, 保密的向量求和等. 但关于保密计算向量等分量数的研究成果还很少, 且主要研究向量分量在有全...     

Formal anonymity models for efficient privacy-preserving joins

Organizations, such as federally-funded medical research centers, must share de-identified data on their consumers to publicly accessible repositories to adhere to regulatory requirements. Many repositories are managed by third-parties and it is often unknown if records received from disparate organizations correspond to the same individual. Failure to resolve this issue can lead to biased (e.g., double counting of identical records) and underpowered (e.g., unlinked records of different data types) investigations. In this paper, we present a secure multiparty computation protocol that enables record joins via consumers’ encrypted identifiers. Our solution is more practical than prior secure join models in that data holders need to interact with the third party one time per data submission. Though technically feasible, the speed of the basic protocol scales quadratically with the number of records. Thus, we introduce an extended version of our protocol in which data holders append k-anonymous features of their consumers to their encrypted submissions. These features facilitate a more efficient join computation, while providing a formal guarantee that each record is linkable to no less than k individuals in the union of all organizations’ consumers. Beyond a theoretical treatment of the problem, we provide an extensive experimental investigation with data derived from the US Census to illustrate the significant gains in efficiency such an approach can achieve.     

适用于智能环境的高效安全云辅助模式匹配协议

以机器学习、人工智能、物联网等技术所构建的智能环境正在改变人们的生活、工作及思维方式.智能环境下数据存储和处理的方式也在不断改变,其中安全和效率是2个重要的因素.就安全而言,在数据共享的前提下保护隐私势在必行.就效率而言,智能环境中存在诸多资源受限的设备,针对这些设备如何设计高效的算法或协议直接决定其可行性.从以上2个需求出发,研究适用于智能环境中的安全高效模式匹配问题.传统的安全模式匹配协议中模式持有方需要执行大量的公钥操作,因此不适用于手机等资源受限设备作为模式持有方的场景.首次在双云服务器辅助的安全两方计算模型下给出安全模式匹配协议的功能函数,并基于茫然传输(oblivious transfer, OT)给出协议的具体构造.假设云服务器和参与方之间不合谋,协议在半诚实敌手模型下是安全的.协议需要4轮交互,模式方仅需要执行少量的异或操作,而复杂的OT协议主要集中在数据库方和云服务器之间.此外,使用OT扩展(OT extension)技术可以将所有OT协议的数量从O(nm)降至O(k),其中n和m是数据库方和模式方的输入长度,k是OT扩展协议中基础OT的数目,其远小于nm.     

一种高效的检测相似重复记录的方法

如何消除数据库中的重复信息是数据质量研究中的一个热课题。文中提出了一种高效的基于N－Gram的检测相似重复记录的方法,主要工作有：（1）提出了一种高效的基于N－Gram的聚类算法,该算法能适应常见的拼写错误从而较好地聚类相似重复记录,复杂度仅为O（N）;同时提出该算法的改进形式,使其在检测的同时能自动校正单词的插入、删除错误、提高检测精度。（2）采用了一种高效的应用无关的Pair-wise比较算法,该算法以单词间的编辑距离为基础,通过计算两记录中单间的编辑距离来判断记录的相似与否。（3）给出了一种改进的优先队列算法来准确地聚类相似重复记录,该算法使用固定大小的优先队列顺序扫描已排序的记录,通过比较当前记录和队列中记录的距离来聚类相似重复记录,此外,该文构造了合适的实验环境并作了大量的算法实验,在此基础上,文中分析了大量、翔实的实验结果从而验证了算法的科学性。     

On private Hamming distance computation

Finding similarities between two datasets is an important task in many research areas, particularly those of data mining, information retrieval, cloud computing, and biometrics. However, maintaining data protection and privacy while enabling similarity measurements has become a priority for data owners in recent years. In this paper, we study the design of an efficient and secure protocol to facilitate the Hamming distance computation between two semi-honest parties (a client and a server). In our protocol design, both parties are constrained to ensure that no extra information will be revealed other than the computed result (privacy is protected) and further, the output of the protocol is according to the prescribed functionality (correctness is guaranteed). In order to achieve these requirements, we utilize a multiplicative homomorphic cryptosystem and include chaff data into the computation. Two experimental results in this paper demonstrate the performance of both the client and the server.     

结构化加密图的最短路径查询

随着云计算的快速发展, 数据用户将大量图数据外包给云以节约存储和管理成本。然而, 外包数据的安全隐私问题是云计算面临的一大挑战。由于云是半诚实的, 为保护敏感信息的隐私安全, 数据拥有者希望在将图数据外包给云服务器之前对其加密, 同时保留对加密的图数据进行查询和处理的能力。最短路径查询查找图中给定两节点之间的最短路径, 是图应用中最基础的查询类型之一。目前已有许多研究者提出一系列高效的方案, 以支持加密图上近似或精确最短距离查询、约束最短距离查询和 top-k 最近关键字查询, 但支持最短路径查询的方案较少, 且已有方案的存储与时间开销较大。本文提出一种支持在加密图上进行两节点间最短路径查询的结构化加密图方案。在本方案中, 我们基于 2-Hop 标签技术构造支持有向图上最短路径查询的标签索引并加密, 然后将加密的标签外包给云服务器。 利用改进的保序编码算法编码距离值, 实现加法运算和值的比较, 提高最短路径查询的效率。在查询阶段, 通过递归式地计算两节点间最短路径上的第一条边和最后一条边, 最终输出完整的最短路径。安全性和性能分析证明本文方案是安全有效的, 能以较小的存储和较高的查询效率实现两节点间的最短路径查询并保护图数据的隐私。     

公平理性委托计算协议

已存在的安全计算集合关系的协议大多基于公钥加密算法,因此很难再嵌入到带有属性关系的公钥加密或密文搜索中.针对该问题,本文给出了非加密方法安全计算集合包含关系和集合交集的2个协议.我们首先利用（n,n）秘密共享的思想分别将原来2个问题转化为集合相等问题.在此基础上,结合离散对数,构造了安全计算集合包含关系的协议1和集合交集的协议2.最后的分析显示：我们的方案没有使用任何公钥加密方法,在保持了较优通信复杂性的同时,便于作为一种子模块嵌入到带有集合操作关系的公钥加密体制或者密文搜索体制中,从而丰富这些方案的功能.     

云环境下集合隐私计算

多方保密计算是网络空间安全与隐私保护的关键技术,基于同态加密算法的多方保密计算协议是解决云计算安全的一个重要工具.集合隐私计算是多方保密计算的一个基本问题,具有广泛的应用.现有的集合隐私计算方案多是基于两方的情况,基于多方的方案较少,效率较低,且这些方案都不能扩展到云计算平台.本文首先设计了一种新的编码方案,根据新的编码方案和同态加密算法在云计算环境下构造了一个具有普遍适用性且抗合谋的保密计算集合并集问题解决方案.该方案中的同态加密算法既可以是加法同态又可以是乘法同态的加密算法.本文进一步利用哥德尔编码和ElGamal公钥加密算法构造了一种适用于云计算的高效集合并集计算方案.这些方案还可以对多个集合中的所有数据进行保密排序,并证明这些方案在半诚实模型下是安全的.本文中的方案经过简单改造,也可以保密地计算多个集合的交集.     

Fast schemes for online record linkage

The process of integrating large volumes of data coming from disparate data sources, in order to detect records that refer to the same entities, has always been an important problem in both academia and industry. This problem becomes significantly more challenging when the integration involves a huge amount of records and needs to be conducted in a real-time fashion to address the requirements of critical applications. In this paper, we propose two novel schemes for online record linkage, which achieve very fast response times and high levels of recall and precision. Our proposed schemes embed the records into a Bloom filter space and employ the Hamming Locality-Sensitive Hashing technique for blocking. Each Bloom filter is hashed to a number of hash tables in order to amplify the probability of formulating similar Bloom filter pairs. The main theoretical premise behind our first scheme relies on the number of times a Bloom filter pair is formulated in the hash tables of the blocking mechanism. We prove that this number strongly depends on the distance of that Bloom filter pair. This correlation allows us to estimate in real-time the Hamming distances of Bloom filter pairs without performing the comparisons. The second scheme is progressive and achieves high recall, upfront during the linkage process, by continuously adjusting the sequence in which the hash tables are scanned, and also guarantees, with high probability, the identification of each similar Bloom filter pair. Our experimental evaluation, using four real-world data sets, shows that the proposed schemes outperform four state-of-the-art methods by achieving higher recall and precision, while being very efficient.     

Symmetric cryptographic solution to Yao’s millionaires’ problem and an evaluation of secure multiparty computations

Secure multiparty computation has become a central research focus in the international cryptographic community and in the future likely will represent an integral part of computing science. Protocols for Yao’s millionaires’ problem provide the building blocks for many secure multiparty computation protocols, which makes their efficiency critical. Unfortunately, all known protocols for Yao’s millionaires’ problem employ public key cryptography and thus are inefficient. This article constructs a new efficient solution to Yao’s millionaires’ problem based on symmetric cryptography. We first develop an efficient protocol for set-inclusion problems, which has independent interest for secure multiparty computations. The privacy-preserving property of the solution is demonstrated by a well-accepted simulation paradigm. To compare the security levels of different solutions, we propose a new security paradigm that quantitatively captures the security levels of different solutions and can determine which secure multiparty computation solution is preferable. This article thus provides an important supplement to the simulation paradigm. Together with the simulation paradigm, it offers a complete security evaluation benchmark for multiparty computations.     

基于RSA签名的优化公平交换协议

公平性是电子商务协议的基本安全要求.RSA是应用最为广泛的公钥密码体制之一.公平交换协议可以使得参与交换的双方以公平的方式交换信息,这样,要么任何一方都可以得到对方的信息,要么双方都得不到对方的信息.分析了现有的公平交换协议构造方法、体系结构及其在实用性和效率方面存在的问题.在此基础上,利用精心构造的扩环中可公开验证的、加密的RSA签名,提出了一种完全基于RSA签名方案的优化公平交换协议,并对其安全性和效率进行了证明和分析.分析表明,提出的方案是简洁、高效、安全的.