An Improved Multiple to One Fully Homomorphic Encryption on the Integers

The public key of the integer homomorphic encryption scheme which was proposed by Van Dijk et al. is long, so the scheme is almost impossible to use in practice. By studying the scheme and Coron’s public key compression technique, a scheme which is able to encrypt n bits plaintext once was obtained. The scheme improved the efficiency of the decrypting party and increased the number of encrypting parties, so it meets the needs of cloud computing better. The security of the scheme is based on the approximate GCD problem and the sparse-subset sum problem.

An Unbounded Fully Homomorphic Encryption Scheme Based on Ideal Lattices and Chinese Remainder Theorem

We propose an unbounded fully homomorphic encryption scheme, i.e. a scheme that allows one to compute on encrypted data for any desired functions without needing to decrypt the data or knowing the decryption keys. This is a rational solution to an old problem proposed by Rivest, Adleman, and Dertouzos [1] in 1978, and to some new problems that appeared in Peikert [2] as open questions 10 and open questions 11 a few years ago. Our scheme is completely different from the breakthrough work [3] of Gentry in 2009. Gentry’s bootstrapping technique constructs a fully homomorphic encryption (FHE) scheme from a somewhat homomorphic one that is powerful enough to evaluate its own decryption function. To date, it remains the only known way of obtaining unbounded FHE. Our construction of an unbounded FHE scheme is straightforward and can handle unbounded homomorphic computation on any refreshed ciphertexts without bootstrapping transformation technique.

A Fully Homomorphic Encryption Scheme with Better Key Size

Fully homomorphic encryption is faced with two problems now.One is candidate fully homomorphic encryption schemes are few.Another is that the efficiency of fully homomorphic encryption is a big question.In this paper,we propose a fully homomorphic encryption scheme based on LWE,which has better key size.Our main contributions are:(1) According to the binary-LWE recently,we choose secret key from binary set and modify the basic encryption scheme proposed in Linder and Peikert in 2010.We propose a fully homomorphic encryption scheme based on the new basic encryption scheme.We analyze the correctness and give the proof of the security of our scheme.The public key,evaluation keys and tensored ciphertext have better size in our scheme.(2) Estimating parameters for fully homomorphic encryption scheme is an important work.We estimate the concert parameters for our scheme.We compare these parameters between our scheme and Bra 12 scheme.Our scheme have public key and private key that smaller by a factor of about logq than in Bra12 scheme.Tensored ciphertext in our scheme is smaller by a factor of about log2 q than in Bra 12 scheme.Key switching matrix in our scheme is smaller by a factor of about log3 q than in Bra 12 scheme.

A Privacy-Preserving Federated Learning Algorithm for Intelligent Inspection in Pumped Storage Power Station

As a distributed machine learning architecture,Federated Learning(FL)can train a global model by exchanging users’model parameters without their local data.However,with the evolution of eavesdropping techniques,attackers can infer information related to users’local data with the intercepted model parameters,resulting in privacy leakage and hindering the application of FL in smart factories.To meet the privacy protection needs of the intelligent inspection task in pumped storage power stations,in this paper we propose a novel privacy-preserving FL algorithm based on multi-key Fully Homomorphic Encryption(FHE),called MFHE-PPFL.Specifically,to reduce communication costs caused by deploying the FHE algorithm,we propose a self-adaptive threshold-based model parameter compression(SATMPC)method.It can reduce the amount of encrypted data with an adaptive thresholds-enabled user selection mechanism that only enables eligible devices to communicate with the FL server.Moreover,to protect model parameter privacy during transmission,we develop a secret sharing-based multi-key RNS-CKKS(SSMR)method that encrypts the device’s uploaded parameter increments and supports decryption in device dropout scenarios.Security analyses and simulation results show that our algorithm can prevent four typical threat models and outperforms the state-of-the-art in communication costs with guaranteed accuracy.

Novel Homomorphic Encryption for Mitigating Impersonation Attack in Fog Computing

Fog computing is a rapidly growing technology that aids in pipelining the possibility of mitigating breaches between the cloud and edge servers.It facil-itates the benefits of the network edge with the maximized probability of offering interaction with the cloud.However,the fog computing characteristics are suscep-tible to counteract the challenges of security.The issues present with the Physical Layer Security(PLS)aspect in fog computing which included authentication,integrity,and confidentiality has been considered as a reason for the potential issues leading to the security breaches.In this work,the Octonion Algebra-inspired Non-Commutative Ring-based Fully Homomorphic Encryption Scheme(NCR-FHE)was proposed as a secrecy improvement technique to overcome the impersonation attack in cloud computing.The proposed approach was derived through the benefits of Octonion algebra to facilitate the maximum security for big data-based applications.The major issues in the physical layer security which may potentially lead to the possible security issues were identified.The potential issues causing the impersonation attack in the Fog computing environment were identified.The proposed approach was compared with the existing encryption approaches and claimed as a robust approach to identify the impersonation attack for the fog and edge network.The computation cost of the proposed NCR-FHE is identified to be significantly reduced by 7.18%,8.64%,9.42%,and 10.36%in terms of communication overhead for varying packet sizes,when compared to the benchmarked ECDH-DH,LHPPS,BF-PHE and SHE-PABF schemes.

新形态对称密码算法研究

安全多方计算、全同态加密和零知识证明是解决数据安全问题的重要密码技术,其快速发展和应用需求驱动,引发了新形态对称密码的设计与安全性分析.本文系统介绍适宜安全多方计算、全同态加密和零知识证明的新形态对称密码算法.首先梳理新形态对称密码算法的应用需求和设计目标;然后归类介绍新形态对称密码算法的设计理念和最新的安全性评估结果;最后总结探讨新形态对称密码算法的设计特点以及面临的问题.

全同态加密软硬件加速研究进展

全同态加密(FHE)是一种重计算、轻交互的多方安全计算协议。在基于全同态加密的计算协议中,尽管计算参与方之间无需多轮交互与大量通信,加密状态下的密态数据处理时间通常是明文计算的10~3~10~6倍,极大地阻碍了这类计算协议的实际落地;而密态数据上的主要处理负担是大规模的并行密码运算和运算所必须的密文及密钥数据搬运需求。该文聚焦软、硬件两个层面上的全同态加密加速这一研究热点,通过系统性地归类及整理当前领域中的文献,讨论全同态加密计算加速的研究现状与展望。

全同态加密技术的研究现状及发展路线综述

随着物联网、云计算、人工智能的应用与普及,数据安全与隐私保护成为人们关注的焦点。全同态加密,作为隐私安全问题的有效解决办法,允许对加密数据执行任意同态计算,是一种强大的加密工具,具有广泛的潜在应用。该文总结了自2009年以来提出全同态加密方案,并根据方案的核心技术划分成4条技术路线,分析讨论了各类方案的关键构造,算法优化进程和未来发展方向。首先,全面介绍了全同态加密相关的数学原理,涵盖了全同态加密方案的基础假设和安全特性。随后,按照4条全同态加密方案的技术路线,归纳了加密方案的结构通式,总结了自举算法的核心步骤,讨论了最新研究进展,并在此基础上综合分析比较了各类方案的存储效率及运算速度。最后,展示了同态算法库对每条技术路线下加密方案的应用实现情况,分析了在当前时代背景下全同态加密方案的机遇与挑战,并对未来的研究前景做出了展望。

全同态加密应用的编码技术综述

全同态加密允许在不解密的前提下对密文进行任意操作,为数据“可用不可见”的需求提供了绝佳的解决思路.由于效率和安全性原因,全同态加密的明文空间通常被定义在多项式环或者有限域代数结构上.目前实用化的全同态加密方案分为以BGV、BFV和CKKS为代表的第二代和以FHEW、TFHE为代表的第三代全同态加密方案,其中第二代方案的基础计算类型包括代数加法和代数乘法,而第三代方案的核心计算单元是门自举.但是现实应用涉及的数据类型包含整数、浮点数、复数、矩阵、向量等,函数计算类型包括多项式函数和三角、求逆、比较等各种非多项式函数.因此在应用全同态加密时,必须设计高效的消息编码和函数计算编码以适用于实际计算任务,提高整体运行效率.本文系统地总结了目前第二代全同态加密涉及的消息编码,并分析了一些常用函数在不同全同态加密方案下的的同态计算编码策略,最后对卷积神经网络推理和对称加密算法(AES)两个实例给出了具体分析和测试.

基于双联盟链的电网分布式异构数据共享研究

电网异构数据分布较为广泛,难以实现有效共享,导致数据共享时延增加、吞吐量变小。为了解决这些问题,提出了基于双联盟链的电网分布式异构数据共享方法。利用双联盟链平台开源代码配置数据共享环境,生成新区块。使用哈希值函数检验区块数据,将同一个属性的异构节点放置在同一通道中。引入全同态加密技术分割明文,获取明文序列,实现单节点跨链数据共享。考虑各个节点隐私情况,采用安全多方计算方式处理相似异构数据,保证异构数据一致性,计算所有节点、局部节点权重,保证数据共享安全性,实现多节点跨链数据共享,从而达到电网分布式异构数据加密共享的目标。实验结果表明,该方法的最大共享时延为10 s,最大吞吐量为460 bit,数据共享效果良好。

云计算环境下基于全同态加密的神经网络分类预测研究

在云计算环境下,数据挖掘应用中的数据共享和服务外包在产生巨大财富的同时,也带来了隐私泄漏的风险,其信息安全问题亟待解决。对云计算环境下神经网络预测分类服务外包中存在的风险以及全同态加密算法的应用进行了分析,使用平方函数作为神经网络中的非线性激活函数,设计并实现了一种密文域的数据分类预测方案,并对该方案运行过程中的5个主要步骤:模型训练、模型加密、数据加密、密文计算和解密结果进行详细描述。设计的测试实验结果验证了该方案的可行性,保证了模型和数据在整个神经网络处理过程中不被泄漏,可有效保障数据和模型的安全与隐私。

基于CKKS全同态加密的用电数据隐私计算

国家电网有限公司提出要提高电网数字化水平,加快数据要素流通,构建能源数字生态圈,支撑各类智慧能源产品和业务创新。然而,数据的安全和隐私问题是数据开放、数据交易的重大障碍,是支撑国家电网的数字化转型战略必须攻克的课题。为此,基于CKKS全同态加密,提出并实现了包含三大参与方(数据提供者、服务提供者、研究者或商业使用者)的用电数据隐私计算分析框架,采用基于模拟的方法证明了上述方案的安全性,分析了基于CKKS隐私计算的时空复杂度,并以负荷曲线聚类作为典型应用场景进行误差分析,以证明所提框架的实用性。

Efficient Security Sequencing Problem over Insecure Channel Based on Homomorphic Encryption

In the field of sequencing of secret number,an important problem is how to establish an efficient and secure protocol for sorting the secret number.As a powerful tool in solving privacy sequencing problems,secure multipart computation is more and more popular in anonymous voting and online auction.In the present study,related secure computation protocol for sequencing problem is not many by far.In order to improve the efficiency and safety,we propose a security sequencing protocol based on homomorphic encryption.We also give analysis of correctness and security to highlight its feasibility.

可复用Garbling的注解

Garbling是一个有着多重应用的密码原语,主要适用于权力受限的场景,比如安全多方计算(MPC)、属性加密(ABE)、函数加密(FE)、不可区分混淆(IO)等等.2013年以前的garbling方案都是一次性garbling,GKP+13和Agr17提出了可复用garbling.我们曾经指出,可复用garbling并没有获得新的应用场景,它仍然是一个一次性garbling.本文继续讨论可复用garbling的可用性和效率.本文指出以下两点:(1)即使可复用garbling被当作一次性garbling使用,也常常是不可用的,它只能用于两个基本场景中的基本场景二,不能用于基本场景一.比如,它不能用于安全多方计算(MPC).(2)即使可复用garbling被当作一次性garbling用于基本场景二,没有证据表明它比原来的一次性garbling效率更高.

基于CKKS加密方案的区块链集成风险评价模型

针对区块链平台中存在的多方数据交互不可信以及隐私数据易泄露等问题,基于CKKS(Cheon-KimKim-Song)全同态加密方案,提出了一种集成风险评价模型,把同态加密算法应用到风险评价中,将多种评价模型与同态加密结合起来.首先,利用三角模糊综合评价方法确定各评价指标的权重,通过多种评价方法处理分布式数据库中的样本数据,获得相关节点对同一交易事件的风险评价结果;其次,利用公钥对评价结果进行加密并进行同态运算,获得密文综合评价结果,以避免风险评价过程中的数据泄露;再次,利用私钥对评价结果进行解码,获得明文综合评价结果;最后,选取5000个中欧班列企业的样本数据作为案例,利用决策树模型、Adaboost模型、Bagging模型、ExtraTree极端随机数模型、GBDT(gradient boosting regression trees)模型、KNN(K-nearest neighbor)模型、随机森林模型、SVM(support vector machine)模型等最为常见的评价模型进行风险评价,并将经CKKS方案加密后的综合评价结果与明文直接计算的综合评价结果和经BFV(Brakerski-Fan-vercauteren)方案加密后的综合评价结果进行了对比.结果表明:该集成风险评价模型具有普适性,对较为常见的评价模型均能适用;模型的综合评价结果误差率较小,与实际结果的误差率均在10-9以内;与BFV方案加密后的结果相比,经CKKS方案加密后的结果误差率小于前者的十万分之一,评价结果更为准确.

魔方派:面向全同态加密的存算模运算加速器设计

全同态加密(FHE)由于其可以实现隐私数据的计算,大大提高了数据的安全性而在医疗诊断、云计算、机器学习等领域取得了广泛的关注。但是全同态密码高昂的计算代价阻碍了其广泛应用。即使经过算法和软件设计优化,FHE全同态加密中一个整数明文的密文数据规模可以达到56 MByte,端侧生成的密钥最大都会达到11 k Byte。密文以及密钥数据规模过大引起严重的计算和访存瓶颈。存内计算(PIM)是一个解决该问题的有效方案,其完全消除了内存墙的延迟和功耗问题,在端侧计算大规模数据时更具优势。利用存内计算加速全同态计算的工作已经被广泛研究,但是全同态加密端侧的执行过程由于耗时的模运算也面临着执行时间的瓶颈。该文分析了BFV方案加密、解密、密钥生成操作中各个关键算子的计算开销,发现模计算的计算开销平均占比达到了41%,延迟占比中访存占97%,因此,该文提出一个名为魔方派(M^(2)PI)的基于静态随机存取存储器(SRAM)存内计算的模运算加速器设计。实验结果表明,该文所提加速器相比CPU中模计算有1.77倍的计算速度提升以及32.76倍能量的节省。

BFV-MKFHE:基于BFV的多密钥全同态加密方案设计

传统的全同态加密方案仅支持对单一密钥加密的数据计算,无法适应多用户场景下的应用需求,多密钥全同态加密可以对不同密钥(用户)下的密文进行运算,最终密文由所有相关用户联合解密.目前在多密钥全同态加密领域针对BFV加密系统的研究较少,为了将BFV同态加密体制拓展到多密钥应用领域,本文设计了一种基于BFV的多密钥全同态加密方案BFV-MKFHE.本方案以CZW17中基于BGV的MKFHE方案为基础,首先将BGV加密系统的结构修改为BFV的结构,减去了每一层的模数转换环节,简化了算法运算过程;其次在重线性化过程中,构造用户私钥的组合密文来生成计算密钥,并利用模数提高技术来优化重线性化过程,降低了同态运算过程中产生的噪声,提高了计算密钥生成效率;最后通过修改加密过程的取整方式,减少了计算冗余.本方案中完成一次同态乘法解密时产生的噪声值减小为CDKS19方案的1/p,生成计算密钥的密文尺寸与CDKS19方案相同,较CZW17方案和LZY+19方案约减小1/4,且无需对用户私钥的密文进行扩展,运算效率更高.

新形态伪随机函数研究

随着云计算模式的普及应用,对密文数据的安全外包计算的研究已是必然趋势,由此,潜在的密文数据的安全计算和隐私保护问题愈加受到业界和学界的关注。新形态伪随机函数(Pseudorandom Function,PRF)作为解决密文安全计算与检索的重要工具之一,已是当前密码学的研究热点。当前,以密文安全计算为目标,结合全同态加密(Fully Homomorphic Encryption,FHE)与格密码、门限密码、安全多方计算(Multiparty Computing,MPC)和PRF等密码学原语,对新形态伪随机函数的研究主要集中在三方面:1)格基限制隐藏的PRF可验证性研究;2)格基受限PRF适应性安全研究;3)格基多点隐私可穿刺PRF应用性研究。因此,文章从PRF的可验证性、安全性和应用性三方面,较为全面地介绍当前重要的研究成果。

Modified Multi-Key Fully Homomorphic Encryption Based on NTRU Cryptosystem without Key-Switching

The Multi-Key Fully Homomorphic Encryption (MKFHE) based on the NTRU cryptosystem is an important alternative to the post-quantum cryptography due to its simple scheme form,high efficiency,and fewer ciphertexts and keys.In 2012,Lopez-Alt et al.proposed the first NTRU-type MKFHE scheme,the LTV12 scheme,using the key-switching and modulus-reduction techniques,whose security relies on two assumptions:the Ring Learning With Error (RLWE) assumption and the Decisional Small Polynomial Ratio (DSPR) assumption.However,the LTV12and subsequent NTRU-type schemes are restricted to the family of power-of-2 cyclotomic rings,which may affect the security in the case of subfield attacks.Moreover,the key-switching technique of the LTV12 scheme requires a circular application of evaluation keys,which causes rapid growth of the error and thus affects the circuit depth.In this paper,an NTRU-type MKFHE scheme over prime cyclotomic rings without key-switching is proposed,which has the potential to resist the subfield attack and decrease the error exponentially during the homomorphic evaluating process.First,based on the RLWE and DSPR assumptions over the prime cyclotomic rings,a detailed analysis of the factors affecting the error during the homomorphic evaluations in the LTV12 scheme is provided.Next,a Low Bit Discarded&Dimension Expansion of Ciphertexts (LBD&DEC) technique is proposed,and the inherent homomorphic multiplication decryption structure of the NTRU is proposed,which can eliminate the key-switching operation in the LTV12 scheme.Finally,a leveled NTRU-type MKFHE scheme is developed using the LBD&DEC and modulus-reduction techniques.The analysis shows that the proposed scheme compared to the LTV12 scheme can decrease the magnitude of the error exponentially and minimize the dimension of ciphertexts.

An Efficient Encrypted Speech Retrieval Based on Unsupervised Hashing and B+ Tree Dynamic Index

Existing speech retrieval systems are frequently confronted with expanding volumes of speech data.The dynamic updating strategy applied to construct the index can timely process to add or remove unnecessary speech data to meet users’real-time retrieval requirements.This study proposes an efficient method for retrieving encryption speech,using unsupervised deep hashing and B+ tree dynamic index,which avoid privacy leak-age of speech data and enhance the accuracy and efficiency of retrieval.The cloud’s encryption speech library is constructed by using the multi-threaded Dijk-Gentry-Halevi-Vaikuntanathan(DGHV)Fully Homomorphic Encryption(FHE)technique,which encrypts the original speech.In addition,this research employs Residual Neural Network18-Gated Recurrent Unit(ResNet18-GRU),which is used to learn the compact binary hash codes,store binary hash codes in the designed B+tree index table,and create a mapping relation of one to one between the binary hash codes and the corresponding encrypted speech.External B+tree index technology is applied to achieve dynamic index updating of the B+tree index table,thereby satisfying users’needs for real-time retrieval.The experimental results on THCHS-30 and TIMIT showed that the retrieval accuracy of the proposed method is more than 95.84%compared to the existing unsupervised hashing methods.The retrieval efficiency is greatly improved.Compared to the method of using hash index tables,and the speech data’s security is effectively guaranteed.