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Inference-Aware State Reconstruction for Industrial Metaverse under Synchronous/Asynchronous Short-Packet Transmission

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IEEE Transactions on Communications 2025年

作者： Xiong, Qinqin Cao, Jie Zhu, Xu Jiang, Yufei Pappas, Nikolaos Harbin Institute of Technology School of Electronic and Information Engineering Shenzhen518055 China The Shenzhen Key Laboratory of Wireless AloT Communications Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen Guangdong China Linkoping University Department of Computer and Information Science Linköping58183 Sweden

We consider a real-time state reconstruction system for industrial metaverse. The time-varying physical process states in real space are captured by multiple sensors via wireless links, and then reconstructed in virtual space. In this paper, we use the spatial-temporal correlation of the sensor data of interest to infer the real-time data of the target sensor to reduce the mean squared error (MSE) of reconstruction for industrial metaverse under short-packet transmission (SPT). Both synchronous and asynchronous transmission modes for multiple sensors are considered. It is proved that the average reconstruction MSE and average block error probability (BLEP) have a positive correlation under inference with synchronous transmission scheme, whereas they have a negative correlation under inference with asynchronous transmission scheme in certain conditions. Additionally, the average reconstruction MSE decreases monotonically with the mean squared spatial correlation (MSSC), which characterizes the global spatial correlation level. With a high BLEP or long transmission period, even under weak MSSC, the inference scheme still significantly reduces the average reconstruction MSE compared to the no inference case. Moreover, closed-form MSSC thresholds are derived for the superiority regions of the inference with synchronous transmission and inference with asynchronous transmission schemes, respectively. Adaptations of blocklength and time shift of asynchronous transmission are conducted to minimize the average reconstruction MSE. Simulation results show that the two inference schemes outperform the no inference case, with an average MSE reduction of more than 50%. © 1972-2012 IEEE.

关键词： Packet networks

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Manifold Constraint Regularization for Remote Sensing Image Generation

arXiv

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arXiv 2023年

作者： Su, Xingzhe Zheng, Changwen Qiang, Wenwen Wu, Fengge Zhao, Junsuo Sun, Fuchun Xiong, Hui The National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China The National Key Laboratory of Space Integrated Information System Department of Computer Science and Technology Tsinghua University Beijing China The Hong Kong University of Science and Technology China

Generative Adversarial Networks (GANs) have shown notable accomplishments in remote sensing domain. However, this paper reveals that their performance on remote sensing images falls short when compared to their impressive results with natural images. This study identifies a previously overlooked issue: GANs exhibit a heightened susceptibility to overfitting on remote sensing images. To address this challenge, this paper analyzes the characteristics of remote sensing images and proposes manifold constraint regularization, a novel approach that tackles overfitting of GANs on remote sensing images for the first time. Our method includes a new measure for evaluating the structure of the data manifold. Leveraging this measure, we propose the manifold constraint regularization term, which not only alleviates the overfitting problem, but also promotes alignment between the generated and real data manifolds, leading to enhanced quality in the generated images. The effectiveness and versatility of this method have been corroborated through extensive validation on various RS datasets and GAN models. The proposed method not only enhances the quality of the generated images, reflected in a 3.13% improvement in Frechet Inception Distance (FID) score, but also boosts the performance of the GANs on downstream tasks, evidenced by a 3.76% increase in classification accuracy. The source code is available at https://***/rootSue/Manifold-RSGAN. Copyright © 2023, The Authors. All rights reserved.

关键词： Remote sensing

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Towards Privacy-Preserving and Practical Vaccine Supply Chain based on Blockchain

Towards Privacy-Preserving and Practical Vaccine Supply Chai...

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Asia Joint Conference on information security (Asia JCIS)

作者： Baodong Wen Yujue Wang Yong Ding Hai Liang Changsong Yang Decun Luo Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Hangzhou Innovation Institute of Beihang University Hangzhou China Cyberspace Security Research Center Pengcheng Laboratory Shenzhen China

Vaccines play a vital role in safeguarding people’s health and improving their well-being. With the increase in the variety and number of infectious diseases, the importance of vaccination cannot be underestimated. Despite being responsible for overseeing the entire lifecycle of vaccines, the current vaccine supply chain is encountering certain issues. There is no effective mechanism for sharing vaccine data, while the privacy of recipient data cannot be guaranteed. Also, different entities involved in the supply chain manage their data separately, leading to data barriers that hinder the effective supervision and tracking of vaccine data. This paper designs a vaccine whole process management system that employs a consortium blockchain to facilitate trustworthy collaboration among various entities involved in the vaccine supply chain. The system leverages data encryption to preserve data privacy and promote seamless data sharing, thereby overcoming data silos among participants. Additionally, smart contracts are utilized to establish uniform data operation protocols, while a regulator-friendly vaccine management mechanism is proposed to enable comprehensive supervision of the entire vaccine supply process. Experiments demonstrate the effectiveness of the proposed system.

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DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

arXiv

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arXiv 2024年

作者： Deng, Kaijun Zheng, Dezhi Xie, Jindong Wang, Jinbao Xie, Weicheng Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China Department of Computer Science University of Exeter United Kingdom

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code will be publicly available at https://***/CVI-SZU/DEGSTalk. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

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Remote Sensing Images Semantic Segmentation Method Based on Improved Nested UNet

Remote Sensing Images Semantic Segmentation Method Based on ...

引用

2022 International Conference on Geographic information and Remote Sensing technology, GIRST 2022

作者： Li, Zhongyu Liu, Yang Kuang, Yin Wang, Huajun Liu, Cheng College of Geophysics Chengdu University of Technology Chengdu610059 China College of Computer Science Chengdu Normal University Chengdu611130 China Key Laboratory of interior Layout optimization and Security Institutions of Higher Education of Sichuan Province Sichuan Chengdu611130 China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu610106 China Artificial Intelligence Key Laboratory of Sichuan Province Zigong643000 China

ISBN: (纸本)9781510662186

With the development of remote sensing technology, remote sensing images of buildings are of great significance in urban planning, disaster response, and other directions. When we use a neural network containing batch normalization layers for semantic segmentation, the neural network is sensitive to batch size and has low segmentation accuracy for occluded and dense buildings. This paper proposes a method for building segmentation in remote sensing images based on Nested UNet (UNet++) deep neural network. First, the UNet++ network is used to extract features, and the Group Normalization (GN) method is used instead of Batch Normalization (BN) to alleviate the model's sensitivity to batch size. Then the weighted combination of Cross-Entropy Loss (CELoss) and DiceLoss is used as the loss function to improve the feature extraction ability of the neural network for unbalanced buildings. Finally, experiments are carried out on the WHUBuilding dataset. The experimental results show that the improved model (UNet++-GN) improves Mean Intersection over Union (MIoU) and Mean Pixel Accuracy (Macc) by 12.16% and 2.92%, respectively, compared with the original model (UNet++-BN). © 2023 SPIE.

关键词： Feature extraction

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SecSCS: A User-Centric Secure Smart Camera system Based on Blockchain

SecSCS: A User-Centric Secure Smart Camera System Based on B...

引用

International Conference on Distributed Computing systems

作者： Xinyuan Qian Hongwei Li Haoyong Wang Guowen Xu Shengmin Xu Ju Ren School of Computer Science and Engineering University of Electronic Science and Technology of China Chongqing Key Laboratory of Automated Reasoning and Cognition Department of Computer Science City University of Hong Kong College of Computer and Cyber Security Fujian Normal University Department of Computer Science and Techology Tsinghua University

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Smart cameras have gained immense popularity in commercial markets for their safety and security capabilities. Yet, the prevalent design of these intelligent camera systems often compels users to cede control of their data to poten-tially untrusted service providers, such as cloud services. This relinquishment can lead to unauthorized data access by these intermediaries, posing significant security and privacy risks. The conventional solutions have been to employ privacy-enhancing technologies to bypass these intermediaries, but at the cost of increased overhead for video streaming and sharing. In our study, we introduce SecSCS, a user-centric, blockchain-based secure camera system that incorporates essential features like video streaming, sharing, deletion, and permission restoration. SecSCS integrates a blockchain-enabled user login protocol with a secure device pairing mechanism that combines visual authorization with blockchain to flexibly manage the device ownership. We utilize blockchain to provide integrity protection for the video clips stored remotely, ensuring the video data remains tamper-proof. Furthermore, we present a video frame compression and a fast video encryption method aimed at boosting the efficiency of smart camera systems. Our evaluations show that, in comparison to the leading decentralized scheme, CaCTUs, SecSCS improves the computational and communication overhead for live streaming by a factor of 12.58 and 11.29, respectively, at a frame rate of 24 fps and a resolution of 720p.

关键词： Authorization Visualization Data privacy Protocols technology Smart cameras Streaming media

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The Power of Bamboo: On the Post-Compromise security for Searchable Symmetric Encryption

arXiv

引用

arXiv 2024年

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2024, CC BY.

关键词： Bamboo

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RAS2P: Remote Attestation via Self-Measurement for SGX-based Platforms

RAS2P: Remote Attestation via Self-Measurement for SGX-based...

引用

2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Ren, Zhengwei Li, Xueting Deng, Li Tong, Yan Xu, Shiwei Tang, Jinshan Wuhan University of Science and Technology School of Computer Science and Technology Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Wuhan University of Science and Technology School of Computer Science and Technology Wuhan China Agricultural University College of Informatics Huazhong Wuhan China George Mason University College of Health and Human Services Department of Health Administration and Policy Fairfax United States

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Remote Attestation (RA) is a security service by which a Verifier (Vrf) can verify the platform state of a remote Prover (Prv). However, in most existing RA schemes, the Prv might be vulnerable to denial of service (DoS) attacks due to the interactive challenge-response methodology while there is no authentication about the challenge. Worse, many schemes cannot effectively detect mobile malware that can be inactive during the on-demand attestation launched by the Vrf. In this paper, we propose a self-measurement RA for SGX-based platforms, which can effectively mitigate DoS attacks and defend against mobile malware. To this end, a two-way identity authentication is first enforced between the Prv and Vrf with the help of a blockchain system, in which a shared session key is also generated. Secondly, trigger conditions of measurements on the Prv's side are time points generated by the Prv self instead of Vrf's requests. The Vrf can retrieve multiple self-measurement results during one execution of the protocol to monitor the Prv's platform over a period of time continuously, which can detect mobile malware effectively. Our scheme utilizes SGX to provide the runtime protection for sensitive information such as session key, self-measurement code, time points of self-measurements, and self-measurement results, making a higher security guarantee. In addition, the session key, time points of self-measurements, and self-measurement code can be changed or upgraded, making our scheme more flexible and scalable. The simulation implementation and results show that our scheme is feasible and practical. © 2022 IEEE.

关键词： Denial-of-service attack

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A Brief Survey on Privacy-Preserving Methods for Graph-Structured Data

A Brief Survey on Privacy-Preserving Methods for Graph-Struc...

引用

International Conference on Image, Vision and Intelligent systems, ICIVIS 2021

作者： Zhang, Yunan Wu, Tao Xian, Xingping Xu, Yuqing Electric Power Research Institute CSG Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China School of Cybersecurity and Information Law Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9789811669620

As one of the main manifestations of big data, graph-structured data widely exists in various fields such as social networks, smart cities, medical health, and finance, and is characterized by high dimension, nonlinear, scale-free, small world, etc. Extensive graph-structured data provides sufficient data resources for scientific research and commercial applications. However, graph data mining not only reveals the intrinsic value of data, but also brings the risk of privacy disclosure. Therefore, how to protect graph-structured data privacy is of great significance. In this paper, we survey the very recent research development on privacy preserving methods for graph-structured data. We introduce the common sensitive information and the related privacy risks in graph data, and elaborate the background knowledge for privacy inference attack. Then, the privacy inference attack methods and privacy preservation methods for graph-structured data are summarized. Finally, the shortcomings of the current research about graph-structured data privacy preservation and the possible research directions are discussed. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Surveys

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Efficient Batch Verification and Privacy-Preserving Data Aggregation Scheme in V2G Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Wang, Congcong Wang, Chen Shen, Jian Vasilakos, Athanasios V. Wang, Bin Wang, Weizheng Department of Software Engineering Nanjing University of Information Science and Technology Nanjing210044 China the Zhejiang Key Laboratory of Digital Fashion and Data Governance the Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Provincial Innovation Center of Advanced Textile Technology Shaoxing312000 China College of Computer Science and Information Technology IAU Saudi Arabia Grimstad4879 Norway Network and Data Security Hangzhou310053 China Computer Science Department City University of Hong Kong Hong Kong the Division of Computer Science University of Aizu Fukushima965–8580 Japan

In recent years, against the backdrop of smart grids, vehicle-to-grid (V2G) interaction has become a hot topic. With the increasing number of new energy vehicles, handling massive amounts of electricity transaction data has become an urgent problem in need of resolution. Ensuring the security of the vast quantities of electricity transaction data has emerged as a significant challenge. Therefore, two data aggregation schemes are proposed in this paper, both supporting batch verification and privacy protection in centralized and distributed scenarios, respectively. Firstly, an improved Paillier cryptosystem is utilized to achieve lightweight encryption in the proposed schemes. In addition, a novel distributed data aggregation structure that combines a large number of geographically distributed fog nodes with centralized cloud servers is introduced. This structure not only meets the requirements of lightweight encryption and resistance against single point failures but also reduces the communication complexity of distributed data aggregation to O(n). According to the security proofs and experimental analysis, it can be demonstrated that both proposed schemes are effective and secure, meeting the lightweight requirements of modern smart grid systems. © 2025 IEEE. All rights reserved,

关键词： Data aggregation

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