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A Privacy Protection Scheme for Consumer Electronics Data Based on Blockchain and AI

IEEE Transactions on Consumer Electronics 2025年

作者： Wang, Luping Yan, Chen Wu, Hongjie Zhu, Fa Kumari, Saru Alenazi, Mohammed J.F State Key Laboratory of Blockchain and Data Security Suzhou University of Science and Technology Suzhou215009 China Zhejiang University Hangzhou310027 China Suzhou University of Science and Technology Suzhou215009 China Nanjing Forestry University College of Information Science College of Artificial Intelligence Nanjing210037 China Chaudhary Charan Singh University Department of Mathematics Uttar Pradesh Meerut250001 India Department of Computer Engineering Riyadh11451 Saudi Arabia

As information technology advances, a significant amount of data related to consumer electronics has emerged, the consumer data is commonly stored on cloud servers, resulting in frequent data leaks and privacy breaches. To tackle these challenges, we propose a novel privacy protection scheme that combines encryption and blockchain technologies. This approach integrates Searchable Encryption, Blockchain, and Artificial Intelligence (AI) to facilitate efficient access control and policy enforcement. By employing a decentralized network structure, our scheme minimizes reliance on a central authority, thereby enhancing system security and reliability. User information is independently stored in both blockchain and cloud systems, with digital signatures ensuring data accuracy. To further bolster user privacy, we implement attribute-based encryption for access management and utilize AI for real-time monitoring of potential security threats and suspicious access patterns. Our scheme also features an attribute revocation mechanism to protect sensitive information from unauthorized access. Additionally, customized smart contracts promote equitable interactions between users and cloud service providers. Lastly, the theoretical analyses and experimental results validate that our solution is both secure and efficient in safeguarding consumer data, highlighting its significant innovative contributions to data privacy protection. © 1975-2011 IEEE.

关键词： Sensitive data

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JAX-CNV:A Whole-genome Sequencing-based Algorithm for Copy Number Detection at Clinical Grade Level

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Genomics, Proteomics & Bioinformatics 2022年第6期20卷 1197-1206页

作者： Wan-Ping Lee Qihui Zhu Xiaofei Yang Silvia Liu Eliza Cerveira Mallory Ryan Adam Mil-Homens Lauren Bellfy Kai Ye Charles Lee Chengsheng Zhang Precision Medicine Center The First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China The Jackson Laboratory for Genomic Medicine FarmingtonCT 06032USA School of Cyber Science and Engineering Xi’an Jiaotong UniversityXi’an 710049China Department of Pathology and Laboratory Medicine Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPA 19104USA School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Department of Life Sciences Ewha Womans UniversitySeoul 03760South Korea

We aimed to develop a whole-genome sequencing(WGS)-based copy number variant(CNV)calling algorithm with the potential of replacing chromosomal microarray assay(CMA)for clinical ***-CNV is thus developed for CNV detection from WGS *** performance of this CNV calling algorithm was evaluated in a blinded manner on 31 samples and compared to the 112 CNVs reported by clinically validated CMAs for these 31 *** result showed that JAX-CNV recalled 100%of these ***,JAX-CNV identified an average of 30 CNVs per individual,representing an approximately seven-fold increase compared to calls of clinically validated *** validation of 24 randomly selected CNVs showed one false positive,i.e.,a false discovery rate(FDR)of 4.17%.A robustness test on lowercoverage data revealed a 100%sensitivity for CNVs larger than 300 kb(the current threshold for College of American Pathologists)down to 10×*** CNVs larger than 50 kb,sensitivities were 100%for coverages deeper than 20×,97%for 15×,and 95%for 10×.We developed a WGS-based CNV pipeline,including this newly developed CNV caller JAX-CNV,and found it capable of detecting CMA-reported CNVs at a sensitivity of 100%with about a FDR of 4%.We propose that JAX-CNV could be further examined in a multi-institutional study to justify the transition of first-tier genetic testing from CMAs to ***-CNV is available at https://***/TheJacksonlaboratory/JAX-CNV.

关键词： Copy number variant Chromosomal microarray assay Whole-genome sequencing JAX-CNV Genetic testing

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A CPK-Based Identity Authentication Scheme for IoT

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computer systems science & Engineering 2022年第3期40卷 1217-1231页

作者： Mingming Zhang Jiaming Mao Yuanyuan Ma Liangjie Xu Chuanjun Wang Ran Zhao Zhihao Li Lu Chen Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information&Telecommunication BranchNanjing210008China State Grid Key Laboratory of Information&Network Security Institute of Information and CommunicationGlobal Energy Interconnection Research InstituteNanjing210003China Anhui Jiyuan Software Co.Ltd SGITGHefei230088China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Engineering Research Center of Broadband Wireless Communication Technology of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

As the power Internet of Things(IoT)enters the security construction stage,the massive use of perception layer devices urgently requires an identity authentication scheme that considers both security and *** existing public key infrastructure(PKI)-based security authentication scheme is currently difficult to apply in many terminals in *** key distribution and management costs are high,which hinders the development of power IoT security *** Public key(CPK)technology uses a small number of seeds to generate unlimited public *** is very suitable for identity authentication in the power Internet of *** this paper,we propose a novel identity authentication scheme for power *** scheme combines the physical unclonable function(PUF)with improved CPK technology to achieve mutual identity authentication between power IoT terminals and *** proposed scheme does not require third-party authentication and improves the security of identity authentication for power ***,the scheme reduces the resource consumption of power IoT *** improved CPK algorithm solves the key collision problem,and the third party only needs to save the private key and the public key *** results show that the amount of storage resources occupied in our scheme is *** proposed scheme is more suitable for the power IoT.

关键词： Power Internet of Things terminal equipment identity authentication CPK PUF

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Knowledge Distillation based Lightweight Adaptive Graph Convolutional Network for Skeleton-based action recognition 11

Knowledge Distillation based Lightweight Adaptive Graph Conv...

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11th International Conference on information technology in Medicine and Education, ITME 2021

作者： Qiu, Zhong-Xiang Zhang, Hong-Bo Lei, Qing Du, Ji-Xiang Huaqiao University Department of Computer Science and Technology Xiamen China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China

ISBN: (纸本)9781665406796

Skeleton-based human action recognition has received extensive attention due to its easy access to human skeleton data. However, the current mainstream skeleton-based action recognition methods have more or less the problem of overlarge parameters, which makes it difficult for these methods to meet the requirements of timeliness and accuracy. To solve this problem, we improve attention-enhanced adaptive graph convolutional neural network (AAGCN) to obtain a high-precision improved AAGCN (IAAGCN), and use it as teacher model to conduct knowledge distillation of our lightweight IAAGCN (LIAAGCN). The results of the tests on the NTU-RGBD dataset are validated by knowledge distillation to allow LIAAGCN to maintain good accuracy while keeping the parameters small. © 2021 IEEE.

关键词： Convolution

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GraphEdge: Dynamic Graph Partition and Task Scheduling for GNNs Computing in Edge Network

arXiv

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arXiv 2025年

作者： Xiao, Wenjing Shi, Chenglong Chen, Miaojiang Liu, Zhiquan Chen, Min Song, H. Herbert Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China School of Computer Electronics and Information Guangxi University Nanning530004 China School of Computer Science and Engineering South China University of Technology Guangzhou510006 China Pazhou Laboratory Guangzhou510330 China College of Cyber Security Jinan University Guangzhou510632 China

With the exponential growth of Internet of Things (IoT) devices, edge computing (EC) is gradually playing an important role in providing cost-effective services. However, existing approaches struggle to perform well in graph-structured scenarios where user data is correlated, such as traffic flow prediction and social relationship recommender systems. In particular, graph neural network (GNN)based approaches lead to expensive server communication cost. To address this problem, we propose GraphEdge, an efficient GNN-based EC architecture. It considers the EC system of GNN tasks, where there are associations between users and it needs to take into account the task data of its neighbors when processing the tasks of a user. Specifically, the architecture first perceives the user topology and represents their data associations as a graph layout at each time step. Then the graph layout is optimized by calling our proposed hierarchical traversal graph cut algorithm (HiCut), which cuts the graph layout into multiple weakly associated subgraphs based on the aggregation characteristics of GNN, and the communication cost between different subgraphs during GNN inference is minimized. Finally, based on the optimized graph layout, our proposed deep reinforcement learning (DRL) based graph offloading algorithm (DRLGO) is executed to obtain the optimal offloading strategy for the tasks of users, the offloading strategy is subgraph-based, it tries to offload user tasks in a subgraph to the same edge server as possible while minimizing the task processing time and energy consumption of the EC system. Experimental results show the good effectiveness and dynamic adaptation of our proposed architecture and it also performs well even in dynamic scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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A Federated Learning Network Intrusion Detection system for Multiple Imbalances

A Federated Learning Network Intrusion Detection System for ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yifan Zeng Peijia Zheng Jian Li School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Centralized training of deep learning-based network intrusion detection systems (DLNIDS) raises privacy concerns and incurs huge overhead. Federated learning (FL), while preserving privacy, confronts challenges including size imbalance, heterogeneous and global imbalance. To address these issues, this paper proposes a Self-Balancing Federated learning Network Intrusion Detection system (SBFedNIDS) for accurate and efficient network intrusion detection while preserving privacy and tackling negative impact caused by multiple imbalances resulting from FL. SBFedNΠ)S can efficiently mitigate model bias cause by multiple imbalances and addresses imbalances at data-level via federated cost sensitive learning (FCSL) and client synthetic minority over-sampling technique (CSMOTE), without requiring additional training resources, unlike generative models. Extensive experiments on a benchmark dataset show that SBFedNIDS outperforms baselines in multiple imbalanced scenarios and even surpasses centralized training in several settings.

关键词： Training Privacy Data privacy Accuracy Costs Federated learning Network intrusion detection Signal processing Benchmark testing Speech processing

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High-Resolution Polymerization Network For Multi-Person Pose Estimation 11

High-Resolution Polymerization Network For Multi-Person Pose...

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11th International Conference on information technology in Medicine and Education, ITME 2021

作者： Bai, Yu-Fei Zhang, Hong-Bo Lei, Qing Du, Ji-Xiang Huaqiao University Department of Computer Science and Technology Xiamen China Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Xiamen China

ISBN: (纸本)9781665406796

In this paper, we propose a High-Resolution Polymerization Network (HPN) for Multi-Person Pose Estimation. First, we use the separate channel convolution module High-Resolution Polymerization Block(HPB) in HPN to optimize the input features. The HPB module can segment local features on the channel multiple times and fuse different scales, learn more refined features to extract reliable keypoint coordinate representations. In addition, we observe that there is only a simple vertical identity mapping relationship among the features in the network structure. In order to further utilize the high-resolution features that contain rich spatial information, we extend the horizontal mapping of network characteristics, use parallel convolutional to link high and low resolution features across scales, enhance feature information by maintaining high-resolution representation, to achieve the purpose of precise keypoint positioning. Our method is evaluated on the COCO data set, and the current method has reached an advanced level. © 2021 IEEE.

关键词： Convolution

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Skeleton Based Action Quality Assessment of Figure Skating Videos 11

Skeleton Based Action Quality Assessment of Figure Skating V...

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11th International Conference on information technology in Medicine and Education, ITME 2021

作者： Li, Hui-Ying Lei, Qing Zhang, Hong-Bo Du, Ji-Xiang Huaqiao University Department of Computer Science and Technology Xiamen China Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Xiamen China

ISBN: (纸本)9781665406796

Action quality assessment(AQA) aims at achieving automatic evaluation the performance of human actions in video. Compared with action recognition problem, AQA focuses more on subtle differences both in spatial and temporal dimensions during the whole executing process of actions. However, most existing AQA methods tried to extract features directly from RGB videos through a 3D ConvNets, which makes the features mixed with useless scene information. To overcome this problem, We propose a deep pose feature learning AQA method that captured detailed and meaningful representations for skeleton information to discover the subtle motion difference of AQA problem. We first apply pose estimation method to obtain human skeleton data from RGB videos. Then a spatio-temporal graph convolutional network (ST-GCN) is employed to extract the dynamic changes of skeleton data and obtain the representative pose features. Finally, a regressor composed of three fully connected layers is developed to reduce the dimension of the obtained pose features and predict the final score. Experiments on MIT figure skating dataset have been extensively conducted, and the results demonstrate that the proposed method has achieved improvements that outperformed current state-of-the-art methods. © 2021 IEEE.

关键词： Musculoskeletal system

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Active Power Control for Off-Shore Wind Turbines Against FDI Attack

Active Power Control for Off-Shore Wind Turbines Against FDI...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Xuguo Jiao Hao Luo Liuyang Jiang Lin Wang Xin Wang Zuqiang Huang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China School of Information and Control Engineering Qingdao University of Technology Qingdao China Zhejiang Windey Co. Ltd. Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Academy of Sciences Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Civil Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Recently, offshore wind power has rapidly developed due to its low environmental interference and abundant wind resources. However, due to its remote geographical location and high demand for communication infrastructure, off-shore wind turbines (OWTs) are vulnerable to potential threats from false data injection (FDI) attack. FDI attack can interfere with active power control strategies by tampering with the rotor speed data measured by sensors and pose a threat to the stability of the power grid. To ensure that OWTs can accurately track the active power instructions set by wind farm's control center under FDI attack, this paper proposes a rotor speed control (RSC) strategy based on Kalman filtering technology (RSC- KF). This strategy can estimate the rotor speed of the wind turbine in real-time, and then a residual detection and compensation mechanism is designed to effectively detect and correct attacked rotor speed information caused by FDI attack. Further, based on the corrected rotor speed, the RSC method is developed, which can flexibly switch control strategies according to different rotor speed values, ensuring the efficient operation of OWTs under different wind speed conditions. The proposed approach's effectiveness is proved via simulations conducted on the OpenFAST platform.

关键词： Filtering Velocity control Power control Rotors Power system stability Stability analysis Real-time systems Wind turbines Kalman filters Cyberattack

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Mel-S3R: Combining Mel-spectrogram and self-supervised speech representation with VQ-VAE for any-to-any voice conversion

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Speech Communication 2023年 151卷 52-63页

作者： Jichen Yang Yi Zhou Hao Huang School of Cyber Security Guangdong Polytechnic Normal University Guangzhou China School of Electronics and Information Engineering South China Normal University Foshan China Department of Electrical and Computer Engineering National University of Singapore Singapore School of Information Science and Engineering Xinjiang University and Xinjiang Provincial Key Laboratory of Multilingual Information Technology Urumqi China

The self-supervised speech representation (S3R) has succeeded in many downstream tasks, such as speaker recognition and voice conversion thanks to its high-level information. Voice conversion (VC) is a task to convert the source speech into a target speaker’s voice. Though S3R features effectively encode content and speaker information, spectral features contain low-level acoustic information that is complementary to the S3R. As a result, solely relying on the S3R features for VC may not be optimal. In order to seek speech representation carrying both high-level learned information and low-level spectral details for VC, we proposed a three-level attention to combine Mel-spectrogram (Mel) and S3R, denoted as Mel-S3R. In particular, S3R features are high-level learned representations extracted by a pre-trained network with self-supervised learning. Whereas Mel is the spectral feature representing the acoustic information. Then the proposed Mel-S3R is used as the input of any-to-any VQ-VAE-based VC and the experiments are performed as a downstream task. Objective metrics and subjective listening tests have demonstrated that the proposed Mel-S3R speech representation facilitates the VC framework to achieve robust performance in terms of both speech quality and speaker similarity.

关键词： Self-supervised learning Voice conversion (VC) Self-attention VQ-VAE

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