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IEEE International Conference on Image Processing

作者： Wei Wang Mingwei Zhang Ziwen Wu Qianxi Chen Yue Li Trusted AI Laboratory College of Computer Science Nankai University Tianjin Key Laboratory of Network and Data Security Technology Tianjin China

With the development of distributed machine learning and federated learning, the solution to the data island problem is promoted. People use computer clusters to train machine learning models on data distributed in different regions. In the early stage of research, researchers usually assume that the data sets of each node are independent identically distribution (IID), but this is a strong assumption, which is challenging to meet in practical applications. Therefore, research on non-IID has become a hot spot in recent years. However, there is no uniform standard for designing and evaluating non-IID scenarios. This paper proposes a Frechet distance-independent non-IID distribution dataset metric MDFD. And we conducted experiments on different types of distributed machine-learning methods in different non-IID scenarios to verify the effectiveness of MDFD.

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A security Analysis of Honey Vaults

A Security Analysis of Honey Vaults

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IEEE Symposium on security and Privacy

作者： Fei Duan Ding Wang Chunfu Jia College of Cyber Science Nankai University Tianjin China Key Laboratory of Data and Intelligent System Security (NKU) Ministry of Education Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin China

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

Honey encryption (HE) protected password vaults (called honey vaults) are promising tools that allow a user to store multiple passwords (called a password vault) and encrypt them with a master password using HE. In case password vaults are somehow leaked and the attackers launch offline password guessing, honey vaults can yield decoy password vaults for incorrect guesses, forcing an offline guessing attacker to interact with the authentication server to identify whether passwords in decrypted vaults are correct or not. Therefore, honey vaults transform the offline guessing attacker into an online guessing attacker, i.e., honey vault distinguishing *** online guessing, attackers can adopt various attacks to perform multiple guesses against multiple vaults, but the existing theoretical message recovery (MR) security for HE only focuses on the advantage of one-time guess against a single vault, which cannot accurately model realistic attackers and thus can not provide practical advice for users’ vault security. To address this issue, we propose a theoretically-grounded optimal strategy for distinguishing attackers, and manage to derive a much tighter upper bound on the advantage against MR security. Particularly, we provide much tighter upper/lower bounds for advantage against HE-related cryptographic security games, i.e., the security of distribution transforming encoder (DTE), known message attack, and known side information attack. This provides a better understanding of the actual security of honey *** better understand the security of honey vault systems, we instantiate our optimal strategy into three practical attacks and propose an encoding attack. Extensive experiments against two major honey vault systems demonstrate that our four attacks can improve the attack success rate by 1.15-4.35 times compared with their counterparts. For the intersection attack, we propose a feature attack against Cheng et al.’s incremental update mechanism (at USENI

关键词： Privacy Upper bound Authentication Passwords Games Transforms Encoding

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Joint Optimization Task Offloading Strategy for Mobile Edge Computing 2

Joint Optimization Task Offloading Strategy for Mobile Edge ...

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2nd IEEE International Conference on Information technology, Big data and Artificial Intelligence, ICIBA 2021

作者： Tang, Xue Wen, Zhan Chen, Jiali Li, Yiquan Li, Wenzao College of Communication Engineer Chengdu University of Information Technology Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu611730 China

ISBN: (纸本)9781665428767

With increase of Internet of Things (IoT) devices, the burden of cloud computing servers is gradually increasing. Mobile Edge Computing (MEC), as an emerging solution, can relieve the pressure of computing and improve the quality of user experience. One of the critical challenges in MEC is to make effective use of communication and computing resources and select the task processing mode reasonably. In this paper, based on the scenario of a single mobile device (MD) to one mobile edge server (MES), we propose a Joint optimization method base on Discrete Binary Particle Swarm Optimization Algorithm (JOBPSO) for system load. The JOBPSO can optimize MD's task offloading, reduce delay and energy consumption. Finally, the simulation results indicate that the performance of JOBPSO in reducing system load is better than the other two traditional offloading methods. © 2021 IEEE.

关键词： Mobile edge computing

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Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6369-6384页

作者： Wang, Xiuhua Wang, Shuai Li, Yiwei Fan, Fengrui Li, Shikang Lin, Xiaodong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients' private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients' privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this article, we propose a differentially private and heterogeneity-robust FL algorithm, named DP-FedCVR to mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposed DP-FedCVR algorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate that DP-FedCVR provides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets. © 2020 IEEE.

关键词： Federated learning

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Integrated Multi-featured Android Malicious Code Detection

Integrated Multi-featured Android Malicious Code Detection

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2019国际计算机前沿大会

作者： Qing Yu Hui Zhao Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin University of Technology

To solve the problem that using a single feature cannot play the role of multiple features of Android application in malicious code detection,an Android malicious code detection mechanism is proposed based on integrated learning on the basis of dynamic and static *** three types of Android behavior characteristics,a three-layer hybrid algorithm was *** it combined the malicious code detection based on digital signature to improve the detection *** digital signature of the known malicious code was extracted to form a malicious sample *** authority that can reflect Android malicious behavior,API call and the running system call features were also *** expandable hybrid discriminant algorithm was designed for the above three types of *** algorithm was tested with machine learning method by constructing the optimal classifier suitable for the above ***,the Android malicious code detection system was designed and implemented based on the multi-layer hybrid *** experimental results show that the system performs Android malicious code detection based on the combination of signature and dynamic and static *** with other related work,the system has better performance in execution efficiency and detection rate.

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Fair electronic voting via bitcoin deposits 5th

Fair electronic voting via bitcoin deposits

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5th International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2017

作者： Wu, Xijuan Wei, Baodian Tian, Haibo Du, Yusong Ma, Xiao School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China Chongqing Key Lab of Computer Network and Communication Technology Chongqing China

ISBN: (纸本)9789811308956

Bitcoin is the most popular decentralized digital currency now in use. Block chain is the basic technology of Bitcoin, providing a trustable ledger that can be publicly verified. Research on distributed applications based on block chain has become a new trend. We propose an electronic voting scheme based on block chain and prime numbers, which can support voting situations for multiple candidates. We design protocols for the Bitcoin voting situation, in which there are n voters and k candidates. Each voter will vote for one candidate. The proposed protocols could guarantee that the candidate who gets the majority voting wins the game and no individual voting information is disclosed. Due to the nature of the block chain, the voting results could not be tampered. It is transparent since the block chain is open to the public for verification. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Deposits

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E2E-MFERC:AMulti-Face Expression Recognition Model for Group Emotion Assessment

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Computers, Materials & Continua 2024年第4期79卷 1105-1135页

作者： Lin Wang Juan Zhao Hu Song Xiaolong Xu Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing210042China School of Network Security Jinling Institute of TechnologyNanjing211169China State Grid Jiangsu Electric Power Company Limited Nanjing210000China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210042China

In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assessstudents’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis,thereby continuouslypromotingthe improvementof teaching ***,most existingmulti-face expressionrecognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance,and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single faceimages, which are of low quality and lack specificity, also restricting the development of this research. This paperaims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable forsmart classrooms, construct a high-quality multi-face expression dataset to support algorithm research, and applythe model to group emotion assessment to expand its application value. To this end, we propose an end-to-endmulti-face expression recognition algorithm model for smart classrooms (E2E-MFERC). In order to provide highqualityand highly targeted data support for model research, we constructed a multi-face expression dataset inreal classrooms (MFED), containing 2,385 images and a total of 18,712 expression labels, collected from smartclassrooms. In constructing E2E-MFERC, by introducing Re-parameterization visual geometry group (RepVGG)block and symmetric positive definite convolution (SPD-Conv) modules to enhance representational capability;combined with the cross stage partial network fusion module optimized by attention mechanism (C2f_Attention),it strengthens the ability to extract key information;adopts asymptotic feature pyramid network (AFPN) featurefusion tailored to classroomscenes and optimizes the head prediction output size;achieves high-performance endto-end multi-face expression detection. Finally, we apply the model to smart classroom group emotion assessmentand provide design refe

关键词： Multi-face expression recognition smart classroom end-to-end detection group emotion assessment

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An Efficient and Provably Secure SM2 key-Insulated Signature Scheme for Industrial Internet of Things

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Computer Modeling in Engineering & Sciences 2024年第1期138卷 903-915页

作者： Senshan Ouyang Xiang Liu Lei Liu Shangchao Wang Baichuan Shao Yang Zhao School of Mechanical Engineering Northwestern Polytechnical UniversityXi’anChina Department of Process and Information Technology Chengdu Aircraft Industrial(Group)Co.Ltd.ChengduChina School of Information and Software Engineering The Network and Data Security Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina

With the continuous expansion of the Industrial Internet of Things(IIoT),more andmore organisations are placing large amounts of data in the cloud to reduce ***,the channel between cloud servers and smart equipment is not trustworthy,so the issue of data authenticity needs to be *** SM2 digital signature algorithm can provide an authentication mechanism for data to solve such ***,it still suffers from the problem of key *** order to address this concern,this study first introduces a key-insulated scheme,SM2-KI-SIGN,based on the SM2 *** scheme boasts strong key insulation and secure *** scheme uses the elliptic curve algorithm,which is not only more efficient but also more suitable for IIoT-cloud ***,the security proof of SM2-KI-SIGN is given under the Elliptic Curve Discrete Logarithm(ECDL)assumption in the random oracle.

关键词： key-insulated SM2 algorithm digital signature Industrial Internet of Things(IIoT) provable security

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New (k, l, m)-verifiable multi-secret sharing schemes based on XTR public key system

arXiv

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arXiv 2020年

作者： Yang, Jing Fu, Fang-Wei Chern Institute of Mathematics and LPMC Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300071 China

Secret sharing was proposed primarily in 1979 to solve the problem of key distribution. In recent decades, researchers have proposed many improvement schemes. Among all these schemes, the verifiable multi-secret sharing (VMSS) schemes are studied sufficiently, which share multiple secrets simultaneously and perceive malicious dealer as well as participants. By pointing out that the schemes presented by Dehkordi and Mashhadi in 2008 cannot detect some vicious behaviors of the dealer, we propose two new VMSS schemes by adding validity check in the verification phase to overcome this drawback. Our new schemes are based on XTR public key system, and can realize GF (p6) security by computations in GF (p2) without explicit constructions of GF (p6), where p is a prime. Compared with the VMSS schemes using RSA and linear feedback shift register (LFSR) public key cryptosystems, our schemes can achieve the same security level with shorter parameters by using trace function. What’s more, our schemes are much simpler to operate than those schemes based on Elliptic Curve Cryptography (ECC). In addition, our schemes are dynamic and threshold changeable, which means that it is efficient to implement our schemes according to the actual situation when participants, secrets or the threshold needs to be *** Codes 94A62, 94A60, 11T71, 68R01 © 2020, CC-BY-NC-ND.

关键词： Shift registers

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New results on vectorial dual-bent functions and partial difference sets

arXiv

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arXiv 2022年

作者： Wang, Jiaxin Fu, Fang-Wei Chern Institute of Mathematics and LPMC Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300071 China

Bent functions f: Vn → Fp with certain additional properties play an important role in constructing partial difference sets, where Vn denotes an n-dimensional vector space over Fp, p is an odd prime. In [2,3], the so-called vectorial dual-bent functions are considered to construct partial difference sets. In [2], Çeşmelio glu et al. showed that for vectorial dual-bent functions F: Vn → Vs with certain additional properties, the preimage set of 0 for F forms a partial difference set. In [3], Çeşmelio glu et al. showed that for a class of Maiorana-McFarland vectorial dual-bent functions F: Vn → Fps, the preimage set of the squares (non-squares) in F∗ps for F forms a partial difference set. In this paper, we further study vectorial dual-bent functions and partial difference sets. We prove that for vectorial dual-bent functions F: Vn → Fps with certain additional properties, the preimage set of the squares (non-squares) in F∗ps for F and the preimage set of any coset of some subgroup of F∗ps for F form partial difference sets. Furthermore, explicit constructions of partial difference sets are yielded from some (non)-quadratic vectorial dual-bent functions. In this paper, we illustrate that almost all the results of using weakly regular p-ary bent functions to construct partial difference sets are special cases of our results. In [2], Çeşmelio glu et al. showed that for a weakly regular p-ary bent function f with f(0) = 0, if f is a so-called l-form with gcd(l − 1, p − 1) = 1 for some integer 1 ≤ l ≤ p − 1, then f is vectorial dual-bent. We prove that the converse also holds, which answers one open problem proposed in [3]. Copyright © 2022, The Authors. All rights reserved.

关键词： Walsh transforms

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