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.

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.

Secure planar convex hull protocol for large-scaled point sets in semi-honest model

Efficiency and scalability are still the bottleneck for secure multi-party computation geometry （SMCG）. In this work a secure planar convex hull （SPCH） protocol for large-scaled point sets in semi-honest model has been proposed efficiently to solve the above problems. Firstly, a novel priva- cy-preserving point-inclusion （PPPI） protocol is designed based on the classic homomorphic encryp- tion and secure cross product protocol, and it is demonstrated that the complexity of PPPI protocol is independent of the vertex size of the input convex hull. And then on the basis of the novel PPPI pro- tocol, an effective SPCH protocol is presented. Analysis shows that this SPCH protocol has a good performance for large-scaled point sets compared with previous solutions. Moreover, analysis finds that the complexity of our SPCH protocol relies on the size of the points on the outermost layer of the input point sets only.

Research on Physical Layer Security in Cognitive Wireless Networks with Multiple Eavesdroppers Based on Resource Allocation Algorithm

With the rapid development of the Internet of Things (IoT), non-Orthogonal Multiple Access (NOMA) technology and cognitive wireless network are two promising technologies to improve the spectral efficiency of the system, which have been widely concerned in the field of wireless communication. However, due to the importance of ownership and privacy protection, the IoT system must provide corresponding security mechanisms. From the perspective of improving the transmission security of CR-NOMA system based on cognitive wireless network, and considering the shortcomings of traditional relay cooperative NOMA system, this paper mainly analyzes the eavesdropping channel model of multi-user CR-NOMA system and derives the expressions of system security and rate to improve the security performance of CR-NOMA system. The basic idea of DC planning algorithm and the scheme of sub-carrier power allocation to improve the transmission security of the system were introduced. An algorithm for DC-CR-NOMA was proposed to maximize the SSR of the system and minimize the energy loss. The simulation results show that under the same complexity, the security and speed of the system can be greatly improved compared with the traditional scheme.

个人信息的内生安全机制及其实现

个人信息处理的技术性、隐蔽性,使自上而下的“命令式”监管范式失效。数字时代,个人信息治理的重点是防止个人信息处理者滥用数据权力威胁国家安全、盘剥和操控个体。为有效控制数据权力这一风险源,国家应当督促个人信息处理者建立健全涵盖制度保障、组织程序保障的个人信息内生安全机制。通过监管激活个人信息处理者内部的自我规制机制,增强其内部的组织控制和行为规范化程度,消减个人信息处理的负外部性,实现个人信息处理的正和博弈。通过监管督促个人信息处理者强化内部治理机制,严格落实个人信息保护影响评估制度,遵从“三同步”要求将个人信息保护法规定的各项义务嵌入产品“代码”中,防控个人信息处理过程中妨害个人自主性、减损人格尊严的风险。

面向6G的生成对抗网络研究进展综述

人工智能(AI,artificial intelligence)与通信技术的深度融合是6G网络的典型特征。一方面,AI为6G网络发展注入了新动力,能够有效利用网络运行产生的历史数据,使网络具备自维护、自优化的功能,加速了网络智能化进程。另一方面,6G网络丰富的场景和大规模的物联设备入网应用为AI提供了广阔的应用渠道和海量的训练数据,使AI能够更好地训练和部署,充分发挥AI的内在优势,为用户提供更加优质的智能服务。尽管如此,在一些实际应用中,受复杂环境的影响,存在数据样本收集困难、收集成本高和样本普适性不足等问题,难以充分发挥AI的性能优势。为此,学术界和工业界将生成对抗网络(GAN,generative adversarial network)引入无线网络的设计中,利用GAN强大的特征学习和特征表达能力产生大量模拟实际的生成样本,实现无线数据库的扩充,从而有效提升面向无线网络的AI模型的泛化能力。由于其优秀的性能表现,以GAN为代表的生成式模型在无线网络领域受到越来越多的关注,并迅速发展成为6G网络新的研究热点。首先,综述了GAN的原理及其改进衍生模型,对各种衍生模型的框架及优缺点进行了分析归纳;然后,综述了这些模型在无线网络领域的研究及应用现状;最后,面向6G网络的需求展望了GAN在6G网络中的研究趋势,为未来的研究提供了一些有价值的探索。

基于工业控制模型的非参数CUSUM入侵检测方法

为解决日趋严重的工业控制系统(industrial control system,ICS)信息安全问题,提出一种针对工业控制网络的非参数累积和(cumulative sum,CUSUM)入侵检测方法.利用ICS输入决定输出的特性,建立ICS的数学模型预测系统的输出,一旦控制系统的传感器遭受攻击,实际输出信号将发生改变.在每个时刻,计算工业控制模型的预测输出与传感器测量信号的差值,形成基于时间的统计序列,采用非参数CUSUM算法,实现在线检测入侵并报警.仿真检测实验证明,该方法具有良好的实时性和低误报率.选择适当的非参数CUSUM算法参数τ和β,该入侵检测方法不但能在攻击对控制系统造成实质伤害前检测出攻击,还对监测ICS中的误操作有一定帮助.

Secure blockchain bidding auction protocol against malicious adversaries

In recent years,with the development of blockchain,electronic bidding auction has received more and more attention.Aiming at the possible problems of privacy leakage in the current electronic bidding and auction,this paper proposes an electronic bidding auction system based on blockchain against malicious adversaries,which uses the secure multi-party computation to realize secure bidding auction protocol without any trusted third party.The protocol proposed in this paper is an electronic bidding auction scheme based on the threshold elliptic curve cryptography.It can be implemented without any third party to complete the bidding auction for some malicious behaviors of the participants,which can solve the problem of resisting malicious adversary attacks.The security of the protocol is proved by the real/ideal model paradigm,and the efficiency of the protocol is analyzed.The efficiency of the protocol is verified by simulating experiments,and the protocol has practical value.

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.

人口老龄化背景下某市医疗机构建筑无障碍水平调查研究

目的为当前老龄化时代和改善老年人的无障碍就医环境提供科学参考。方法2022年12月—2023年2月期间采用随机抽样法,抽取某市10家医疗机构的375名人员作为调查对象,发放自制的《医疗机构患者就医感受综合评分表(无障碍设施类)》调查问卷375份,获得有效问卷356份并进行了信效度检验。问卷设定6个无障碍水平调查指标,每个指标设1至10分,对建筑无障碍水平进行问卷调查,收集的资料采用数据秩和比法和四分图模型进行统计分析。结果某市医疗机构的建筑无障碍平均得分为35.86分,多个指标处于中等水平,提示无障碍水平有待进一步提高。同时,不同医疗机构的经济运营状况对无障碍建设有一定影响,无障碍建设还与当地政府建设规划、工作人员的无障碍建筑意识等因素有关。结论为了应对老龄化挑战,提高老年人就医质量,降低社保成本,某市医疗机构可采取停车位、厕所等方面的无障碍改造,提升无障碍AI技术应用,打造智慧化就医场景等措施;政府需加快完善配套政策法规,加大无障碍设施改造方面的支持力度;高等院校、科研院所等可与医疗机构协同创新,产、学、研、用一体化发展,促进成果转化。

On a Generic Security Game Model

To protect the systems exposed to the Internet against attacks, a security system with the capability to engage with the attacker is needed. There have been attempts to model the engagement/interactions between users, both benign and malicious, and network administrators as games. Building on such works, we present a game model which is generic enough to capture various modes of such interactions. The model facilitates stochastic games with imperfect information. The information is imperfect due to erroneous sensors leading to incorrect perception of the current state by the players. To model this error in perception distributed over other multiple states, we use Euclidean distances between the outputs of the sensors. We build a 5-state game to represent the interaction of the administrator with the user. The states correspond to 1) the user being out of the system in the Internet, and after logging in to the system;2) having low privileges;3) having high privileges;4) when he successfully attacks and 5) gets trapped in a honeypot by the administrator. Each state has its own action set. We present the game with a distinct perceived action set corresponding to each distinct information set of these states. The model facilitates stochastic games with imperfect information. The imperfect information is due to erroneous sensors leading to incorrect perception of the current state by the players. To model this error in perception distributed over the states, we use Euclidean distances between outputs of the sensors. A numerical simulation of an example game is presented to show the evaluation of rewards to the players and the preferred strategies. We also present the conditions for formulating the strategies when dealing with more than one attacker and making collaborations.

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.

Behavior Prediction of Untrusted Relays Based on Nonzero-Sum Game

To keep the secrecy performance from being badly influenced by untrusted relay(UR), a multi-UR network through amplify-and-forward(AF) cooperative scheme is put forward, which takes relay weight and harmful factor into account. A nonzero-sum game is established to capture the interaction among URs and detection strategies. Secrecy capacity is investigated as game payoff to indicate the untrusted behaviors of the relays. The maximum probabilities of the behaviors of relay and the optimal system detection strategy can be obtained by using the proposed algorithm.

基于人工噪声和NOMA安全传输策略分析研究

物理层安全技术利用无线信道特征来实现信息的安全通信,有效克服了传统安全技术依赖于窃听者有限计算能力的缺陷。此外,由于非正交多址接入技术(NOMA)满足多用户同时接入,大大提高了频谱效率而备受关注。针对下行多输入单输出(MISO)系统的安全和速率(SSR)最大化问题,提出了一种基于人工噪声(AN)的NOMA波束成形方案;构建了满足用户最小安全中断概率的约束条件下的SSR最大化问题,并确定最优传输参数闭合表达式。最后,通过数值结果验证了所提方案优于传统的基于AN的正交多址方案和没有AN的NOMA方案。

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.

集合交集元素之和的保密计算

安全多方计算是国际密码学的研究热点之一,保密计算集合交集元素之和问题是安全多方计算比较新的问题之一.该问题在工商业、医疗健康等领域具有重要的理论意义和实用价值.现有解决方案是在有全集情况下设计的,在计算过程中会泄露交集的势且存在一定的误判.在半诚实模型下基于Paillier同态加密算法设计了3个协议,协议1计算共有标识符的数量(即用户标识符交集的势)以及与这些用户相关联的整数值之和,协议2和协议3是在不泄露交集势的情况下计算交集元素关联值之和.整个计算过程不泄露关于协议双方私人输入的任何更多信息.所提协议是在无全集情况下设计的,采用模拟范例证明了所设计协议的安全性,用实验验证协议的高效性.

集合交集元素和的保密计算

安全多方计算作为密码学的重要分支,长期以来主要致力于解决两方或多方参与者隐私数据的联合计算.集合交集元素和的隐私计算作为安全多方计算中的科学计算问题,在保密计算广告转化率中具有重要作用.我们利用保密替换和加密选择求集合的交集,结合Lifted ElGamal加密算法,研究了不同限制下(数据范围较小和数据范围较大)集合交集元素和多方保密计算.本文方案解决两方计算时,Bob只需从Alice发送的数据中选择数据,避免了复杂的模指数运算,且双方不需多次交互,降低了计算成本和通信次数.多方参与计算时,根据加密选择和保密替换的性质,得到集合交集的密文,然后在密文上计算得到集合交集元素的和.通过理论分析和实验证明,本文协议是高效的.最后利用模拟范例证明本文协议是安全的.

基于鲁棒强化学习的配网潮流优化方法

传统深度强化学习在优化配网潮流时易受传感器观测误差等干扰,鲁棒性较差。对此,提出一种基于鲁棒强化学习的配网潮流优化方法。首先以最小化配网网损为目标,电压、潮流越限为安全约束,建立包含分布式发电、储能及负荷单元的配网潮流优化模型。然后将干扰建模为攻击智能体,对配网潮流优化主智能体的观测状态施加扰动,构建双智能体零和博弈鲁棒强化学习模型。最后提出一种双智能体-拉格朗日乘子-信任区域策略优化算法,配网潮流优化主智能体与攻击智能体同步训练、异步学习,相互对抗博弈。仿真结果表明,通过该方法训练的配网潮流优化智能体,能在不同类型的干扰下做出安全决策,提高了配网潮流优化的鲁棒性和安全性。

保密计算交集对应元素和的最大值

安全多方计算是国际密码学的研究热点之一,隐私集合问题是安全多方计算的重要研究方向.本文提出了一个新的安全多方计算问题:Alice和Bob分别拥有集合X={(vi,xi)}_(i=1)^(l_(1))和Y={(w_(j),y_(j))}_(j=1)^(l_(2)),他们想要保密计算v_(i)=w_(j)时的最大值max(x_(i)+y_(j)).该问题在教育、网购等领域具有重要的理论和现实意义.针对这个问题,我们在半诚实模型下提出了两个安全协议.第一个协议基于编码方法和保密移位思想,适用于元组中元素数据范围已知的情况.第二个协议利用Paillier密码算法和添加假元素的方法,适用于元素数据范围未知的情况.最后,我们使用公认的模拟范式证明了两个协议是安全的.

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.