Efficient hardware-based private information retrieval using partial reshuffle

The paper proposes a novel hardware-based private information retrieval （HWPIR） protocol. By partially reshuffling previously accessed items in each round, instead of frequently reshuffling the whole database, the scheme makes better use of shuffled data copies and achieves the computation overhead at O（/N/K）,where N and k are the sizes of the database and secure storage respectively. For securestorage with moderate size, e.g. k = O（/N）, the overhead is 0（4/N）. The result is much better than the state-of-art schemes （as compared to e.g. O（log2N））. Without increasing response time and communication cost, the proposed protocol is truly practicable regardless of the database size. The security and preformance of the protocol is formally analyzed.

Research on privacy information retrieval model based on hybrid homomorphic encryption

The computational complexity of privacy information retrieval protocols is often linearly related to database size.When the database size is large,the efficiency of privacy information retrieval protocols is relatively low.This paper designs an effective privacy information retrieval model based on hybrid fully homomorphic encryption.The assignment method is cleverly used to replace a large number of homomorphic encryption operations.At the same time,the multiplicative homomorphic encryption scheme is first used to deal with the large-scale serialization in the search,and then the fully homomorphic encryption scheme is used to deal with the remaining simple operations.The depth of operations supported by the fully homomorphic scheme no longer depends on the size of the database,but only needs to support the single homomorphic encryption scheme to decrypt the circuit depth.Based on this hybrid homomorphic encryption retrieval model,the efficiency of homomorphic privacy information retrieval model can be greatly improved.

Private membership test protocol with low communication complexity

We introduce a practical method to perform private membership tests.In this method,clients are able to test whether an item is in a set controlled by the server without revealing their query item to the server.After executing the queries,the content of the server's set remains secret.One use case for a private membership test is to check whether a file contains any malware by checking its signature against a database of malware samples in a privacy-preserving way.We apply the Bloom filter and the Cuckoo filter in the membership test procedure.In order to achieve privacy properties,we present a novel protocol based on some homomorphic encryption schemes.In our protocol,we rearrange the data in the set into N-dimensional hypercubes.We have implemented our method in a realistic scenario where a client of an anti-malware company wants to privately check whether a hash value of a given file is in the malware database of the company.The evaluation shows that our method is feasible for real-world applications.We also have tested the performance of our protocol for databases of different sizes and data structures with different dimensions:2-dimensional,3-dimensional,and 4-dimensional hypercubes.We present formulas to estimate the cost of computation and communication in our protocol.

Asymmetric Consortium Blockchain and Homomorphically Polynomial-Based PIR for Secured Smart Parking Systems

In crowded cities,searching for the availability of parking lots is a herculean task as it results in the wastage of drivers’time,increases air pollution,and traffic congestion.Smart parking systems facilitate the drivers to determine the information about the parking lot in real time and book them depending on the requirement.But the existing smart parking systems necessitate the drivers to reveal their sensitive information that includes their mobile number,personal identity,and desired destination.This disclosure of sensitive information makes the existing centralized smart parking systems more vulnerable to service providers’security breaches,single points of failure,and bottlenecks.In this paper,an Improved Asymmetric Consortium Blockchain and Homomorphically Computing Univariate Polynomial-based private information retrieval(IACB-HCUPPIR)scheme is proposed to ensure parking lots’availability with transparency security in a privacy-preserving smart parking system.In specific,an improved Asymmetric Consortium Blockchain is used for achieving secure transactions between different parties interacting in the smart parking environment.It further adopted the method of Homomorphically Computing Univariate Polynomial-based private information retrieval(HCUPPIR)scheme for preserving the location privacy of drivers.The results of IACB-HCUPPIR confirmed better results in terms of minimized computation and communication overload with throughput,latency,and response time with maximized drivers’privacy preservation.Moreover,the proposed fully homomorphic algorithm(FHE)was compared against partial-homomorphic encryption(PHE)and technique without encryption and found that the proposed model has quick communication in allocating the parking slots starting with 24.3 s,whereas PHE starts allocating from 24.7 s and the technique without encryption starts at 27.4 s.Thus,we ensure the proposed model performs well in allocating parking slots with less time and high security with privacy preservation.

隐私保护密文检索技术研究进展

密文检索技术旨在提供密态数据查询服务,提高密文数据的可用性。但目前大多数机制仍存在不同程度的额外信息泄露,容易被攻击者捕获用于恢复明文信息与查询条件。如何强化密文检索中的隐私保护特性,实现信息泄露最小化已成为研究者关注的重点目标。近年来,随着硬件芯片技术与新型密码技术的快速发展,隐私保护密文检索研究方面涌现出了一批新成果,该文主要围绕多样化密文检索、基于可信执行环境的密文检索、隐匿信息检索等研究热点展开阐述,并总结了未来发展趋势。

数据隐私保护关键词检索技术研究综述与应用分析

在现代信息社会,数据隐私保护成为公众关注的焦点。随着互联网用户对个人信息安全的日益重视,信息检索领域的隐私保护研究变得至关重要。隐私保护关键词检索技术旨在在不泄露用户查询意图的情况下,提供安全、保护隐私的检索服务。尽管现有技术在满足基本需求方面取得了进展,但如何在保持效率的同时减少隐私泄露风险,仍是一个挑战。为此,本文对隐私保护关键词检索技术进行了详细回顾,系统地分析了当前主流技术的原理、优势与不足。研究发现,尽管已有技术能够对用户查询进行加密处理,防止敏感信息直接泄露,但在查询模式、访问模式与返回结果之间,仍存在着潜在的隐私泄露风险。针对这一问题,本文提出了一系列改进方向,以增强隐私保护的效果。此外,当前的隐私保护技术在实际应用中面临诸多挑战,这些挑战涉及技术增强、隐私合规等多个方面。通过对隐私保护关键词检索相关前沿技术的融合与创新,有望为解决这些技术问题提供新的思路和方案,推动隐私保护技术向更高水平发展。本文最后对隐私保护关键词检索技术的未来发展方向和创新应用模式进行了展望。

保护搜索内容的字符串模式匹配算法研究

高效的在线字符串模式匹配算法对云数据库检索至关重要,然而搜索内容的泄露会威胁用户隐私。现有的字符串模式匹配算法没有考虑用户搜索内容的保护,可搜索加密方案虽然可以保护用户的搜索内容,但存在索引构建代价大、检索效率低等问题。因此,提出了两种保护用户搜索内容的模式匹配算法:基于分布式点函数的模式匹配(pattern matching based on distributed point function,PMDPF)算法和基于分布式点函数的跳跃式模式匹配(jumping pattern matching based on distributed point function,JPMDPF)算法。PMDPF算法利用指纹函数以及分布式点函数构造模式串真值表,并分发给两台独立的服务器,把搜索中字符对比操作转换为查表操作,从而保护搜索内容。为了提升搜索效率,提出了JPMDPF算法。通过字符跳转,JPMDPF算法以泄露更多信息为代价,其搜索效率比PMDPF算法平均提高了约m倍,其中m为搜索内容长度,同时显著降低了因指纹函数碰撞而导致的误判的概率。实验结果表明,PMDPF算法的搜索效率比基于指纹函数的经典算法提高约5%,并优于现有的可搜索加密方案,PMDPF算法的搜索耗时在搜索内容长度为4时是JPMDPF算法的4.2倍。

基于属性基PIR的隐私保护云图像认证算法

针对云环境图像认证过程中潜在的泄露数据所有者及用户图像特征隐私的问题,提出了一种基于属性基隐私信息检索(private information retrieval,PIR)的隐私保护云图像认证算法,借助属性基PIR完成密态环境下的隐私图像认证。一方面,云服务器存储的由数据所有者提供的图像特征被加密,防止云服务器获取数据所有者隐私信息;另一方面,用户在认证时并不需要提供明文的数据特征,并且所提供的加密属性特征也经过泛化处理,最大程度地保护用户隐私。通过性能分析,在理论上证明了所提算法具有较好的隐私保护能力和算法执行效率;通过人脸和虹膜公共数据集进行了模拟实验测试,实验结果和成因分析证明了所提算法相比同类算法更为优越。

基于加密数据库的高效安全HW-PIR方案

在传统基于硬件的私有信息检索(HW-PIR)方案中,数据库明文记录容易被泄露。为解决该问题,提出基于加密数据库的HW-PIR方案。将数据库记录转化为(0,1)比特流后进行置换,并采用代理重加密算法,实现对密文数据库的查询,从而保证用户的查询隐私不会泄露给数据库服务器,还能防止用户的查询内容与数据库的隐私泄露给安全协处理器及恶意攻击者。效率分析结果表明,该方案的在线查询复杂度为O(1),同时安全处理器预处理阶段的计算量明显降低。

基于可信计算的PIR

介绍了私有信息获取和可信计算的概念,由此引出了基于可信计算PIR的概念并列举了几种现有模型及其性能,并为进一步提高PIR的性能提出了一种新的模型,该模型可以把安全处理器(SC)读写数据库的时间复杂度从O(N3/2)降低到O(cN),其中c是大于1的常数。

PCPIR-V:基于Spark的并行隐私保护近邻查询算法

针对面向大数据的隐私保护查询效率低问题,利用CPIR保护程度高,实现了基于Spark的并行CPIR空间近邻查询隐私保护算法PCPIR-V,提出了基于Row和Bit的并行策略,同时提出并实现了基于聚类的PCPIR-V的缓存优化技术。利用均匀分布、高斯分布和真实数据对PCPIR-V进行了测试验证,在40个核心范围内,PCPIR-V具有良好的扩展性,PCPIR-V缓存优化技术计算时间与朴素PCPIR-V时间相比,平均减少了20%。

User’s Relevance of PIR System Based on Cloud Models

A new method to evaluate fuzzily user＇s relevance on the basis of cloud models has been proposed. All factors of personalized information retrieval system are taken into account in this method. So using this method for personalized information retrieval （PIR） system can efficiently judge multi-value relevance, such as quite relevant, comparatively relevant, commonly relevant, basically relevant and completely non-relevant, and realize a kind of transform of qualitative concepts and quantity and improve accuracy of relevance judgements in PIR system. Experimental data showed that the method is practical and valid. Evaluation results are more accurate and approach to the fact better.

H-PCPIR-V:基于Huffman编码的PCPIR-V优化算法

隐私问题受到越来越多的关注,基于计算的私有信息检索(CPIR)的隐私保护技术允许用户从服务提供商检索数据并且不会泄露查询信息。但是,对于大规模应用,隐私保护技术与可用性之间存在较大差距。针对CPIR算法计算量大、计算时间长而不适合应用于大规模数据隐私保护的问题,提出了基于Spark和Huffman编码的CPIR最近邻查询隐私保护算法(H-PCPIR-V)。H-PCPIR-V算法主要是在数据预处理阶段将最近邻矩阵使用Huffman编码进行压缩减少计算位数,然后通过压缩后矩阵中元素的最大位数对其他元素进行补位,在服务端使用Spark并行框架对查询网格进行并行计算。通过对比实验及实验结果分析发现,相比PCPIR-V算法,H-PCPIR-V算法在服务端的计算代价下降30%左右,客户端的计算代价下降10%左右,通信代价下降40%左右。

PIR-based data integrity verification method in sensor network

Since a sensor node handles wireless communication in data transmission and reception and is installed in poor environment, it is easily exposed to certain attacks such as data transformation and sniffing. Therefore, it is necessary to verify data integrity to properly respond to an adversary's ill-intentioned data modification. In sensor network environment, the data integrity verification method verifies the final data only, requesting multiple communications. An energy-efficient private information retrieval(PIR)-based data integrity verification method is proposed. Because the proposed method verifies the integrity of data between parent and child nodes, it is more efficient than the existing method which verifies data integrity after receiving data from the entire network or in a cluster. Since the number of messages for verification is reduced, in addition, energy could be used more efficiently. Lastly, the excellence of the proposed method is verified through performance evaluation.

匿名网络综述

匿名网络旨在公开网络环境中保护用户通信隐私.自Chaum提出Mix网以来,相关研究在几十年中不断取得进展.如今,匿名网络已发展成以Mix网、DC网或PIR(private information retrieval)为基础,并结合多种设计要素,使之适用于各种应用场景和威胁模型.从匿名概念出发,介绍匿名网络领域的发展情况,分类阐述代表性研究工作及其设计选择,并系统地从匿名性、延迟和带宽开销等角度进行分析.

一种联邦隐私保护用户信息匹配算法

信息化时代,人们在生产和生活中产生了大量的数据,但大多分散在不同的机构或组织中,难以对其建立关联。现有的基于隐私保护可达性查询的用户信息匹配算法大多假设存在一个拥有完整数据集的数据提供方,但现实中数据往往分散在不同参与方中,需要联合所有参与方的数据才能构成完整数据集。为解决这个问题,本文提出了一种联邦隐私保护用户信息匹配算法。在人工数据集中的运行结果显示,本文的方法能在数据分散的场景下进行用户信息匹配,同时加入的本地提前筛选阶段能有效减少待验证自然人数量,减少通信开销。

私有信息检索技术分析对比研究

随着数字化社会和大数据时代下大数据及云计算技术的快速发展,人们对数据安全和隐私保护的需求日渐强烈。私有信息检索技术是构建隐私保护系统的关键密码技术,主要用于保证用户查询数据库时的信息不被泄露。首先,介绍了私有信息检索技术的分类及构造单服务器私有信息检索方案的典型技术路线;其次,重点以近几年基于同态加密算法的单服务器私有信息检索方案为例,从客户端请求大小、服务器响应大小和服务器计算成本的角度进行了对比分析;最后,对目前的私有信息检索技术面临的挑战及其未来的应用进行了简要探讨。

偏振旋转的量子私有信息检索方案

私有信息检索是安全多方计算的重要问题。传统对称私有信息检索(SPIR)的很多假设在量子信息机制下非常脆弱,其安全性受到挑战。目前已提出的量子私有信息检索大都不易实施,该文提出基于偏振旋转的对称量子私有信息检索协议和实验方案。实验方案利用单光子的偏振旋转产生量子密文,不需要复杂的计算,便于硬件实现。协议的无条件安全性由量子力学Heisenberg测不准原理及不可克隆原理保证,并增加了用户诚实性检测,在所提出的非诚实合作模型下,非诚实用户的恶意行为不会造成隐私的泄露,在安全性、鲁棒性、抗第三方窃听等方面均优于经典环境的多种方案。

保护位置隐私和查询内容隐私的路网K近邻查询方法

位置隐私和查询内容隐私是LBS兴趣点(point of interest,简称POI)查询服务中需要保护的两个重要内容,同时,在路网连续查询过程中,位置频繁变化会给LBS服务器带来巨大的查询处理负担,如何在保护用户隐私的同时,高效地获取精确查询结果,是目前研究的难题.以私有信息检索中除用户自身外其他实体均不可信的思想为基本假设,基于Paillier密码系统的同态特性,提出了无需用户提供真实位置及查询内容的K近邻兴趣点查询方法,实现了对用户位置、查询内容隐私的保护及兴趣点的精确检索;同时,以路网顶点为生成元组织兴趣点分布信息,进一步解决了高强度密码方案在路网连续查询中因用户位置变化频繁导致的实用效率低的问题,减少了用户的查询次数,并能确保查询结果的准确性.最后从准确性、安全性及查询效率方面对本方法进行了分析,并通过仿真实验验证了理论分析结果的正确性.

不同程度隐私信息有意遗忘效应的实验研究

以三种不同水平的隐私信息为目标信息,模拟员工招聘情境,采用类似字表的研究范式,探讨了大学生被试对不同程度隐私信息的有意遗忘特点。结果发现:①在模拟员工招聘过程中,实验条件效应显著;②被试对高隐私信息的编码比低隐私信息编码更为精细;③在信息提取时,抑制只能对编码不精细的信息起作用,而对编码精细的信息无明显作用。④与个人有关的某些信息,特别是隐私信息在员工选择的决策中有负面的影响。