A Phase Estimation Algorithm for Quantum Speed-Up Multi-Party Computing

Security and privacy issues have attracted the attention of researchers in the field of IoT as the information processing scale grows in sensor networks.Quantum computing,theoretically known as an absolutely secure way to store and transmit information as well as a speed-up way to accelerate local or distributed classical algorithms that are hard to solve with polynomial complexity in computation or communication.In this paper,we focus on the phase estimation method that is crucial to the realization of a general multi-party computing model,which is able to be accelerated by quantum algorithms.A novel multi-party phase estimation algorithm and the related quantum circuit are proposed by using a distributed Oracle operator with iterations.The proved theoretical communication complexity of this algorithm shows it can give the phase estimation before applying multi-party computing efficiently without increasing any additional complexity.Moreover,a practical problem of multi-party dating investigated shows it can make a successful estimation of the number of solution in advance with zero communication complexity by utilizing its special statistic feature.Sufficient simulations present the correctness,validity and efficiency of the proposed estimation method.

Robust peer-to-peer learning via secure multi-party computation

To solve the data island problem,federated learning(FL)provides a solution paradigm where each client sends the model parameters but not the data to a server for model aggregation.Peer-to-peer(P2P)federated learning further improves the robustness of the system,in which there is no server and each client communicates directly with the other.For secure aggregation,secure multi-party computing(SMPC)protocols have been utilized in peer-to-peer manner.However,the ideal SMPC protocols could fail when some clients drop out.In this paper,we propose a robust peer-to-peer learning(RP2PL)algorithm via SMPC to resist clients dropping out.We improve the segmentbased SMPC protocol by adding a check and designing the generation method of random segments.In RP2PL,each client aggregates their models by the improved robust secure multi-part computation protocol when finishes the local training.Experimental results demonstrate that the RP2PL paradigm can mitigate clients dropping out with no significant degradation in performance.

Secure Multi-party Comparison Protocol and Application

The problem of information comparison is always an important field of SMC.In order to effectively solve the fully equal problem of multi-data for all information,a secure two-party multi-data comparison protocol for equality(STMC)is proposed with the aid of the NTRU encryption.The protocol converts multi-data comparison problem for equality to polynomials comparison for equality.Analysis shows that the protocol is correct and security in semi-honest model.Being STMC as basic building block,a secure multi-party multi-data comparison protocol for equality(SMMC)is proposed.SMMC provides a solution which n participants hope to determine the equality of their private input sets,on the condition of no information leaked.This protocol is proved to be collusion-resistance security.The last,computational complexity and communication complexity of the two protocols are analyzed.It is shown that new protocols have low complexity.We also give applications in the secure multi-party information comparison problem and secure multi-party polynomial comparison problem.

A Secure Microgrid Data Storage Strategy with Directed Acyclic Graph Consensus Mechanism

The wide application of intelligent terminals in microgrids has fueled the surge of data amount in recent years.In real-world scenarios,microgrids must store large amounts of data efficiently while also being able to withstand malicious cyberattacks.To meet the high hardware resource requirements,address the vulnerability to network attacks and poor reliability in the tradi-tional centralized data storage schemes,this paper proposes a secure storage management method for microgrid data that considers node trust and directed acyclic graph(DAG)consensus mechanism.Firstly,the microgrid data storage model is designed based on the edge computing technology.The blockchain,deployed on the edge computing server and combined with cloud storage,ensures reliable data storage in the microgrid.Secondly,a blockchain consen-sus algorithm based on directed acyclic graph data structure is then proposed to effectively improve the data storage timeliness and avoid disadvantages in traditional blockchain topology such as long chain construction time and low consensus efficiency.Finally,considering the tolerance differences among the candidate chain-building nodes to network attacks,a hash value update mechanism of blockchain header with node trust identification to ensure data storage security is proposed.Experimental results from the microgrid data storage platform show that the proposed method can achieve a private key update time of less than 5 milliseconds.When the number of blockchain nodes is less than 25,the blockchain construction takes no more than 80 mins,and the data throughput is close to 300 kbps.Compared with the traditional chain-topology-based consensus methods that do not consider node trust,the proposed method has higher efficiency in data storage and better resistance to network attacks.

基于SMC的隐私保护聚类模型

隐私保护数据挖掘指在实现准确挖掘知识的同时确保敏感数据不泄露。针对垂直分布式数据存储结构的聚类隐私保护问题,提出基于全同态加密协议和数据扰乱方法的隐私保护聚类模型。该模型通过采用安全比较协议解决了垂直分布式聚类的两个隐私保护关键步骤:求解最近簇和判断质心变化,从而实现了数据的有效保护。理论证明了该模型的安全性并分析了其时间复杂度和通信耗量,实验结果表明该隐私保护聚类模型是安全有效的。

基于SMC的分布式隐私保护数据发布研究

针对垂直分布式存储结构的隐私保护数据发布问题,基于元组等价群的概念给出全局k-匿名化的定义和充要条件,采用集合多项式表示方法求解出全局元组ID等价群;并基于多方安全计算的同态加密协议构建了具有隐私性、准确性和公平性的分布式隐私保护k-匿名模型,从而实现了各微数据提供方不泄露本地隐私信息的前提下由半可信第三发布方发布出可供统计分析和数据挖掘等需求的真实有效数据集。实验结果表明,该模型具有很好的安全性、准确性和适用性。

Preserving Privacy of Software-Defined Networking Policies by Secure Multi-Party Computation

In software-defined networking(SDN),controllers are sinks of information such as network topology collected from switches.Organizations often like to protect their internal network topology and keep their network policies private.We borrow techniques from secure multi-party computation(SMC)to preserve the privacy of policies of SDN controllers about status of routers.On the other hand,the number of controllers is one of the most important concerns in scalability of SMC application in SDNs.To address this issue,we formulate an optimization problem to minimize the number of SDN controllers while considering their reliability in SMC operations.We use Non-Dominated Sorting Genetic Algorithm II(NSGA-II)to determine the optimal number of controllers,and simulate SMC for typical SDNs with this number of controllers.Simulation results show that applying the SMC technique to preserve the privacy of organization policies causes only a little delay in SDNs,which is completely justifiable by the privacy obtained.

Concretely ecient secure multi-party computation protocols:survey and mor

Secure multi-party computation(MPC)allows a set of parties to jointly compute a function on their private inputs,and reveals nothing but the output of the function.In the last decade,MPC has rapidly moved from a purely theoretical study to an object of practical interest,with a growing interest in practical applications such as privacy-preserving machine learning(PPML).In this paper,we comprehensively survey existing work on concretely ecient MPC protocols with both semi-honest and malicious security,in both dishonest-majority and honest-majority settings.We focus on considering the notion of security with abort,meaning that corrupted parties could prevent honest parties from receiving output after they receive output.We present high-level ideas of the basic and key approaches for designing di erent styles of MPC protocols and the crucial building blocks of MPC.For MPC applications,we compare the known PPML protocols built on MPC,and describe the eciency of private inference and training for the state-of-the-art PPML protocols.Further-more,we summarize several challenges and open problems to break though the eciency of MPC protocols as well as some interesting future work that is worth being addressed.This survey aims to provide the recent development and key approaches of MPC to researchers,who are interested in knowing,improving,and applying concretely ecient MPC protocols.

Nearly universal and efficient quantum secure multi-party computation protocol

Universality is an important property in software and hardware design.This paper concentrates on the universality of quantum secure multi-party computation(SMC)protocol.First of all,an in-depth study of universality has been conducted,and then a nearly universal protocol is proposed by using the Greenberger-Horne-Zeilinger(GHZ)-like state and stabilizer formalism.The protocol can resolve the quantum SMC problem which can be deduced as modulo subtraction,and the steps are simple and effective.Secondly,three quantum SMC protocols based on the proposed universal protocol:Quantum private comparison(QPC)protocol,quantum millionaire(QM)protocol,and quantum multi-party summation(QMS)protocol are presented.These protocols are given as examples to explain universality.Thirdly,analyses of the example protocols are shown.Concretely,the correctness,fairness,and efficiency are confirmed.And the proposed universal protocol meets security from the perspective of preventing inside attacks and outside attacks.Finally,the experimental results of the example protocols on the International Business Machines(IBM)quantum platform are consistent with the theoretical results.Our research indicates that our protocol is universal to a certain degree and easy to perform.

分布式数据集极差与极值和的保密计算

随着信息通信技术的不断突破与发展,信息获取变得非常便利.与此同时,隐私信息也更容易泄露.将智能领域与安全多方计算技术相结合,有望解决隐私保护问题.目前,安全多方计算已经解决了许多不同隐私保护问题,但还有更多的问题等待人们去解决.对于极差、极值和的安全多方计算问题目前研究的结果很少,极差、极值和作为统计学的常用工具在实际中有广泛的应用,研究极差、极值和的保密计算具有重要意义.提出新编码方法,用新编码方法解决了两种不同的安全多方计算问题,一是极差的保密计算问题,二是极值和的保密计算问题.新编码方法结合Lifted ElGamal门限密码系统,设计多方参与、每方拥有一个数据场景下分布式隐私数据集极差的保密计算协议;将新编码方法稍作改动解决相同场景下保密计算极值和的问题.以此为基础,对新编码方法进一步修改,结合Paillier密码系统设计了两方参与、每方拥有多个数据情况下分布式隐私数据集极差、极值和的保密计算协议.用模拟范例方法证明协议在半诚实模型下的安全性.最后,用模拟实验测试协议的复杂性.效率分析和实验结果表明所提协议简单高效,可广泛用于实际应用中,是解决其他很多安全多方计算问题的重要工具.

联邦学习中的隐私保护技术研究综述

近年来,联邦学习成为解决机器学习中数据孤岛与隐私泄露问题的新思路。联邦学习架构不需要多方共享数据资源,只要参与方在本地数据上训练局部模型,并周期性地将参数上传至服务器来更新全局模型,就可以获得在大规模全局数据上建立的机器学习模型。联邦学习架构具有数据隐私保护的特质,是未来大规模数据机器学习的新方案。然而,该架构的参数交互方式可能导致数据隐私泄露。目前,研究如何加强联邦学习架构中的隐私保护机制已经成为新的热点。从联邦学习中存在的隐私泄露问题出发,探讨了联邦学习中的攻击模型与敏感信息泄露途径,并重点综述了联邦学习中的几类隐私保护技术:以差分隐私为基础的隐私保护技术、以同态加密为基础的隐私保护技术、以安全多方计算(SMC)为基础的隐私保护技术。最后,探讨了联邦学习中隐私保护中的若干关键问题,并展望了未来研究方向。

Secure Two-Party Computational Geometry

Secure Multi-party Computation has been a research focus in international cryptographic community in recent years. In this paper the authors investigate how some computational geometric problems could be solved in a cooperative environment, where two parties need to solve a geometric problem based on their joint data, but neither wants to disclose its private data to the other party. These problems are the distance between two private points, the relation between a private point and a circle area, the relation between a private point and an ellipse area and the shortest distance between two point sets. The paper gives solutions to these specific geometric. problems, and in doing so a building block is developed, the protocol for the distance between two private points, that is also useful in the solutions to other geometric problems and combinatorial problems.

A verifiable privacy-preserving data collection scheme supporting multi-party computation in fog-based smart grid

Incorporation of fog computing with low latency,preprocession(e.g.,data aggregation)and location awareness,can facilitate fine-grained collection of smart metering data in smart grid and promotes the sustainability and efficiency of the grid.Recently,much attention has been paid to the research on smart grid,especially in protecting privacy and data aggregation.However,most previous works do not focus on privacy-preserving data aggregation and function computation query on enormous data simultaneously in smart grid based on fog computation.In this paper,we construct a novel verifiable privacy-preserving data collection scheme supporting multi-party computation(MPC),named VPDC-MPC,to achieve both functions simultaneously in smart grid based on fog computing.VPDC-MPC realizes verifiable secret sharing of users’data and data aggregation without revealing individual reports via practical cryptosystem and verifiable secret sharing scheme.Besides,we propose an efficient algorithm for batch verification of share consistency and detection of error reports if the external adversaries modify the SMs’report.Furthermore,VPDC-MPC allows both the control center and users with limited resources to obtain arbitrary arithmetic analysis(not only data aggregation)via secure multi-party computation between cloud servers in smart grid.Besides,VPDC-MPC tolerates fault of cloud servers and resists collusion.We also present security analysis and performance evaluation of our scheme,which indicates that even with tradeoff on computation and communication overhead,VPDC-MPC is practical with above features.

利用RSA密码体制解决安全多方多数据排序问题

本文研究了姚氏百万富翁问题的一个推广问题,安全多方多数据排序问题:假设有n方P1,P2,…Pn,他们分别拥有一个保密数据集DP1,DP2…DPn{1,2,…,N}.我们对这多个数据集的并集D=DP1∪DP2∪…∪DPn中所有的数据进行一个安全的排序,要求在排序结束后各方能够知道他们各自拥有的数据在D中的次序,并且任意一方都不知道其它方拥有的数据的任何信息.我们提出了一个基于RSA同态密码体制的解决安全多方多数据排序问题的方案,并在半诚实模型下对该协议的正确性、安全性和效率进行了分析.

一类基于安全多方计算的数据挖掘隐私保护模型的可行性分析

阐述安全多方计算(SMC)密码原语在分布式数据挖掘隐私保护中的相关应用后,对方炜炜等人提出的基于SMC的隐私保护数据挖掘模型进行分析,论证该类模型所基于的离散对数公钥加密协议不具有全同态的特性,并用简单实例验证。从而得出该类数据挖掘隐私保护模型是不可行的。

Graham算法求解凸包问题中的隐私保护

凸包问题是构造其他几何形体的有效工具。保护私有信息的凸包求解问题是一个特殊的安全多方计算问题。基于安全多方计算的基础协议,设计了一个保护私有信息的凸包求解协议,解决了基于隐私保护的凸包求解问题,并讨论和分析了其安全性和正确性。

一种保护私有信息的空间离群点检测方法

针对现有空间离群点检测方法难以同时保证数据安全性和检测结果有效性的问题,提出一种隐私保护的空间离群点检测方法。该方法基于空间邻域行为属性值的统计结果及马哈拉诺比斯距离进行空间离群点的检测,通过对基于半诚实模型的安全多方距离、合并向量的中位数及标准化等计算协议的定义和应用,实现私有信息的保护。实验结果表明,该方法在保护隐私信息的同时保证了检测结果的准确性。

安全多方计算的应用研究

安全多方计算(SMC)在解决网络环境下进行合作时的信息安全问题具有重要价值。笔者首先介绍了安全多方计算的基本概念、密码学中相关基础知识,接着介绍和分析了安全多方计算的在电子选举、电子签名和门限签名等方面的基本应用;在该基础上,指出安全多方计算的新的应用方向,即在机械设计工程领域中的应用,如产品的异地设计和制造、敏捷和虚拟制造技术等;提出了值得进一步研究和探讨的、在保护隐私基础上合作的安全多方计算的热点问题。

基于小波变换的分布式隐私保护聚类算法

针对现有隐私保护聚类算法无法满足效率与隐私之间较好折中的问题,提出一种基于安全多方计算(SMC)与数据扰动相结合的分布式隐私保护聚类算法。各数据方用小波变换实现数据压缩和信息隐藏,并用属性列的随机重排来防止数据重构可能产生的信息泄露。该算法仅使用压缩重排后的数据参与分布聚类计算,因此计算量和通信量小,算法效率高,而多重保护措施有效保护了隐私数据。因小波变换具有高保真性,所以聚类精度受小波变换的影响较小。理论分析和实验结果表明,所提算法安全高效,在处理高维数据时全局F测量值和执行效率优于基于Haar小波的离散余弦变换(DCT-H)算法,解决了效率与隐私之间的折中问题。

水平划分多决策表下基于相对粒度的隐私保护属性约简算法

为了解决分布式环境中多个参与方在不共享各自隐私数据的情况下完成全局属性约简计算的问题,提出了一种水平划分多决策表下基于相对粒度的隐私保护属性约简算法。该算法基于相对粒度约简理论实现了分布式环境下全局属性约简的求解,利用半可信第三方与安全多方基础协议,设计了安全多方计算相对粒度协议,使各参与方在不共享其隐私信息的前提下达到集中式属性约简的效果。分析结果表明,该算法是有效可行的。