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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart data, Smartdata 2022

作者： Tao, Junhong Wu, Yongtang Chen, Yuling Guizhou University State Key Laboratory of Public Big Data College of Computer Science and and Technology Guiyang China Guilin University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security Guilin China Weifang University of Science and Technology Blockchain Laboratory of Agricultural Vegetables WeiFang China Weifang Key Laboratory of Blockchain on Agricultural Vegetables WeiFang China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

Secure comparison is an important problem in the field of secure computing. A classic example is the Millionaires' problem, which is securely compares the wealth of two millionaires without revealing their wealth. However most traditional secure comparison protocol requires autonomous computation among the participants. It is necessary to ensure that the model of the participants is a semi-honest model, otherwise the protocol will not be able to execute correctly. In this paper, we introduce a semi-honest judge to improve the Damgård-Geisler-KrOigaard(DGK) comparison protocol by using techniques such as zero-knowledge proof and homomorphic encryption. The improved protocol is not only applicable to the malicious participant model, but also the introduced judge would not get any information of data. At last, the security analysis shows that the proposed scheme in this paper can guarantee the data fairness of the comparison protocol while ensuring the privacy security of the participants. © 2022 IEEE.

关键词： Cryptography

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Rotation-DPeak: Improving Density Peaks Selection for Imbalanced data 1

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8th China Computer Federation International Conference on Big data, CCF BIGdata 2020

作者： Hu, Xiaoliang Yan, Ming Chen, Yewang Yang, Lijie Du, Jixiang The College of Computer Science and Technology Huaqiao University Xiamen China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China

ISBN: (数字)9789811607059

ISBN: (纸本)9789811607042

Density Peak (DPeak) is an effective clustering algorithm. It maps arbitrary dimensional data onto a 2-dimensional space, which yields cluster centers and outliers automatically distribute on upper right and upper left corner, respectively. However, DPeak is not suitable for imbalanced data set with large difference in density, where sparse clusters are usually not identified. Hence, an improved DPeak, namely Rotation-DPeak, is proposed to overcome this drawback according to an simple idea: the higher density of a point p, the larger δ it should have such that p can be picked as a density peak, where δ is the distance from p to its nearest neighbor with higher density. Then, we use a quadratic curve to select points with the largest decision gap as density peaks, instead of choosing points with the largest γ, where γ= ρ× δ. Experiments shows that the proposed algorithm obtains better performance on imbalanced data set, which proves that it is promising. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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LAPAA: A Secure and Lightweight Authentication Protocol for Edge Computing Built on ASCON Algorithm

LAPAA: A Secure and Lightweight Authentication Protocol for ...

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Next Generation data-driven Networks (NGDN), International Conference on

作者： Linghui Zhang Changli Zhou Jingliang Wen College of Computer Science and Technology HuaQiao University Xiamen China Xiamen Key Laboratory of Data Security and Blockchain Technology HuaQiao University Xiamen China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

Facing the challenges of next-generation network security, Mobile Edge Computing (MEC) serves as a decentralized model designed to meet the growing demands for data processing and reduce latency, making its security increasingly critical. However, with the rapid development of edge computing technology, traditional authentication protocols have become inadequate due to their complexity, insufficient protection of user anonymity, and neglect of resource constraints. These issues are at the core of current network security challenges. In response, this paper innovatively proposes LAPAA—a secure and lightweight authentication protocol for Edge computing built on ASCON algorithm. LAPAA effectively addresses the problems found in previous solutions and protects user privacy through anonymity by combining the symmetric encryption algorithm ASCON with asymmetric elliptic curve cryptography (ECC). It employs clever parameter and computation strategies to maintain key consistency between edge devices and MEC servers, while avoiding the leakage of users' real identities. Furthermore, due to the lightweight nature of the ASCON algorithm itself, LAPAA provides superior low-latency performance for the protocol. Additionally, through simulation verification and informal security analysis using the Scyther tool, LAPAA demonstrates its strong defense against various malicious threats and comparative evaluations with other protocols highlight LAPAA’s significant advantages in various aspects. Collectively, this research offers a secure and streamlined authentication strategy for MEC contexts, underscoring LAPAA’s inventive and groundbreaking approach to overcoming prevalent obstacles.

关键词： Privacy Protocols Multi-access edge computing Authentication Network security Elliptic curve cryptography Reliability engineering Robustness Servers Next generation networking

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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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SWAT: ASystem-Wide Approach to Tunable Leakage Mitigation in Encrypted data Stores 50th

SWAT: ASystem-Wide Approach to Tunable Leakage Mitigation in...

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50th International Conference on Very Large data Bases, VLDB 2024

作者： Zheng, Leqian Xu, Lei Wang, Cong Wang, Sheng Hu, Yuke Qin, Zhan Li, Feifei Ren, Kui City University of Hong Kong Hong Kong Nanjing University of Science and Technology China Alibaba Group China Zhejiang University The State Key Laboratory of Blockchain and Data Security China

Numerous studies have underscored the significant privacy risks associated with various leakage patterns in encrypted data stores. While many solutions have been proposed to mitigate these leak ages, they either (1) incur substantial overheads, (2) focus on specific subsets of leakage patterns, or (3) apply the same security notion across various workloads, thereby impeding the attainment of ne-tuned privacy efficiency trade-o s. In light of various detrimental leakage patterns, this paper starts with an investigation into which specific leakage patterns require our focus in the contexts of key-value, range-query, and dynamic workloads, respectively. Sub sequently, we introduce newsecurity notions tailored to the specfic privacy requirements of these workloads. Accordingly, we propose and instantiate Swat, an efficient construction that progressively enables these workloads, while provably mitigating system-wide leakage via a suite of algorithms with tunable privacy-efficiency trade-offs. We conducted extensive experiments and compiled a de tailed result analysis, showing the efficiency of our solution. Swat is about an order of magnitude slower than an encryption-only data store that reveals various leakage patterns and is two orders of magnitude faster than a trivial zero-leakage solution. Meanwhile, the performance of Swat remains highly competitive compared to other designs that mitigate specific types of leakage. © 2024, VLDB Endowment. All rights reserved.

关键词： Information leakage

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Online Discriminative Semantic-Preserving Hashing for Large-Scale Cross-Modal Retrieval 18th

Online Discriminative Semantic-Preserving Hashing for Large-...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Yi, Jinhan He, Yi Liu, Xin Department of Computer Science and Technology Huaqiao University Xiamen361021 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Xiamen China Fujian Key Laboratory of Big Data Intelligence and Security Xiamen China

ISBN: (纸本)9783030891879

Cross-modal hashing has drawn increasing attentions for efficient retrieval across different modalities, and existing methods primarily learn the hash functions in a batch based mode, i.e., offline methods. Nevertheless, the multimedia data often comes in a streaming fashion, which makes the batch based learning methods uncompetitive for large-scale streaming data due to the large memory consumption and calculation. To address this problem, we present an Online Discriminative Semantic-Preserving Hashing (ODSPH) method for large-scale cross-modal retrieval, featuring on fast training speed, low memory consumption and high retrieval accuracy. Within the proposed ODSPH framework, we utilize the newly coming data points to learn the hash codes and update hash functions in a stream manner. When new data comes, the corresponding hash codes are obtained by regressing the class label of the training examples. For hash function, we update it with the accumulated information from each round. Besides, we design a novel momentum updating method to adaptively update the hash function and reduce quantization loss, which can produce discriminative hash codes for high retrieval precision. Extensive experiments on three benchmark datasets show that the proposed ODSPH method improves the retrieval performance over the state-of-the-arts. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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Learning a Mini-Batch Graph Transformer via Two-Stage Interaction Augmentation 27

Learning a Mini-Batch Graph Transformer via Two-Stage Intera...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Li, Wenda Chen, Kaixuan Liu, Shunyu Zheng, Tongya Huang, Wenjie Song, Mingli State Key Laboratory of Blockchain and Security Zhejiang University China School of Software Technology Zhejiang University China Institute of Blockchain and Data Security China Big Graph Center School of Computer and Computing Science Hangzhou City University China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9781643685489

Mini-batch Graph Transformer (MGT), as an emerging graph learning model, has demonstrated significant advantages in semi-supervised node prediction tasks with improved computational efficiency and enhanced model robustness. However, existing methods for processing local information either rely on sampling or simple aggregation, which respectively result in the loss and squashing of critical neighbor information. Moreover, the limited number of nodes in each mini-batch restricts the model’s capacity to capture the global characteristic of the graph. In this paper, we propose LGMformer, a novel MGT model that employs a two-stage augmented interaction strategy, transitioning from local to global perspectives, to address the aforementioned bottlenecks. The local interaction augmentation (LIA) presents a neighbor-target interaction Transformer (NTIformer) to acquire an insightful understanding of the co-interaction patterns between neighbors and the target node, resulting in a locally effective token list that serves as input for the MGT. In contrast, global interaction augmentation (GIA) adopts a cross-attention mechanism to incorporate entire graph prototypes into the target node representation, thereby compensating for the global graph information to ensure a more comprehensive perception. To this end, LGMformer achieves the enhancement of node representations under the MGT paradigm. Experimental results related to node classification on the ten benchmark datasets demonstrate the effectiveness of the proposed method. Our code is available at https://***/l-wd/LGMformer. © 2024 The Authors.

关键词： Contrastive Learning

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Joint-Motion Mutual Learning for Pose Estimation in Video 24

Joint-Motion Mutual Learning for Pose Estimation in Video

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Sifan Chen, Haipeng Yin, Yifang Hu, Sihao Feng, Runyang Jiao, Yingying Yang, Ziqi Liu, Zhenguang College of Computer Science and Technology Jilin University Changchun China A STAR Singapore Singapore Georgia Institute of Technology AtlantaGA United States School of Artificial Intelligence Jilin University Changchun China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Institute of Blockchain and Data Security Hangzhou China

ISBN: (纸本)9798400706868

Human pose estimation in videos has long been a compelling yet challenging task within the realm of computer vision. Nevertheless, this task remains difficult because of the complex video scenes, such as video defocus and self-occlusion. Recent methods strive to integrate multi-frame visual features generated by a backbone network for pose estimation. However, they often ignore the useful joint information encoded in the initial heatmap, which is a by-product of the backbone generation. Comparatively, methods that attempt to refine the initial heatmap fail to consider any spatio-temporal motion features. As a result, the performance of existing methods for pose estimation falls short due to the lack of ability to leverage both local joint (heatmap) information and global motion (feature) dynamics. To address this problem, we propose a novel joint-motion mutual learning framework for pose estimation, which effectively concentrates on both local joint dependency and global pixel-level motion dynamics. Specifically, we introduce a context-aware joint learner that adaptively leverages initial heatmaps and motion flow to retrieve robust local joint feature. Given that local joint feature and global motion flow are complementary, we further propose a progressive joint-motion mutual learning that synergistically exchanges information and interactively learns between joint feature and motion flow to improve the capability of the model. More importantly, to capture more diverse joint and motion cues, we theoretically analyze and propose an information orthogonality objective to avoid learning redundant information from multi-cues. Empirical experiments show our method outperforms prior arts on three challenging benchmarks. © 2024 ACM.

关键词： Motion estimation

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A Rational Delegating Computation Protocol Based on Smart Contract 14th

A Rational Delegating Computation Protocol Based on Smart Co...

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14th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2021

作者： Ma, Juan Chen, Yuling Liu, Guoxu Zhu, Hongliang Peng, Changgen State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and Technology Shouguang China Engineering Lab for Cloud Security and Information Security Center of BUPT Beijing China

ISBN: (纸本)9783030898137

The delegating computation has become an irreversible trend, together comes the pressing need for fairness and efficiency issues. To solve this problem, we leverage game theory to propose a smart contract-based solution. Firstly, according to the behavioral preferences of the participants, we design an incentive contract to describe the motivation of the participants. Secondly, to satisfy the fairness of the rational delegating computation, we propose a delegating computation protocol based on the smart contract. Specifically, rational participants are to gain the maximum utility and reach the Nash equilibrium in the protocol. Besides, we design a reputation mechanism with a reputation certificate, which measures the reputation from multiple dimensions. And the reputation is used to assure the client’s trust in the computing party to improve the efficiency of the protocol. Finally, the analysis results show that the proposed protocol solves the complex traditional verification problem. Meanwhile, we prove the fairness and correctness of the protocol. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Smart contract

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CDS: An Anti-Aging Calibratable Digital Sensor for Detecting Multiple Types of Fault Injection Attacks 24

CDS: An Anti-Aging Calibratable Digital Sensor for Detecting...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Chen, Zhiyuan Yang, Kun Ren, Kui The State Key Laboratory of Blockchain and Data Security Zhejiang University China School of Cyber Science and Technology Zhejiang University China ZJU-Hangzhou Global Scientific and Technological Innovation Center China

ISBN: (纸本)9798400706011

Fault injection attacks (FIAs) are a class of active physical attacks that inject faults into computing devices to deliberately change their intended behaviors for malicious purposes such as security feature circumvention, privilege escalation, secret data extraction by analyzing the erroneous outputs, etc. Existing sensors that detect fault injection attacks are either susceptible to aging or suffer from complicated and costly calibration process. Worse still, most sensors will fail in the presence of dynamic voltage and frequency scaling (DVFS) because they are built upon fixed delay chains and cannot adapt to operating voltage and frequency variations. In this paper, we overcome these limitations by presenting CDS, a delay chain based digital sensor that exploits timing variations of both detector and protected object for detecting multiple types of fault injection attacks. CDS utilizes a calibration module to solve the accuracy degradation caused by aging phenomena and a dynamic adjustment module to acclimatize itself to the need of dynamically adjusting voltage according to operating frequency in the presence of DVFS. To demonstrate its capability, we use CDS to protect the hardware accelerator of PRESENT cryptographic algorithm against voltage and laser glitching attacks. Simulation results based on HSPICE show that (i) CDS can detect 100% of voltage and temperature coordinated glitching attacks with 4.1% early warning;(ii) CDS can detect 100% of laser glitching attacks with 9.1% early warning;(iii) CDS maintains outstanding aging resistance with only 1.1% false alarm rate increase after 7 years of use. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Hardware security

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