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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Ruzhitskaya, Daria Zhluktova, Irina Ponosova, Anastasiya Trefilov, Daniil Chaiwongkhot, Poompong Huang, Anqi Kamynin, Vladimir Makarov, Vadim Russian Quantum Center Skolkovo Moscow121205 Russia NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia Prokhorov General Physics Institute of Russian Academy of Sciences Moscow119991 Russia MIREA - Russian Technological University Moscow119454 Russia Vigo Quantum Communication Center University of Vigo VigoE-36310 Spain Department of Physics Faculty of Science Mahidol University Bangkok10400 Thailand Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Shanghai Branch National Laboratory for Physical Sciences at Microscale CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai201315 China

ISBN: (纸本)9798350345995

Quantum key distribution (QKD) systems provide quantum-safe key exchange. Therefore, complete security analysis of implementations of QKD protocols is in the focus of interest of a worldwide information-security community. For today, a number of QKD loopholes are closed by countermeasures, which are also considered in emerging QKD security evaluation and certification [1-3]. However, new threats to practical QKD implementations are still found, such as the laser-damage attack, which is a powerful hacking strategy. In investigations, CW laser radiation is most often used, but in contrast to it, the interaction of pulsed laser radiation with optical materials may lead to a wide range of effects, like nonlinear effects, dielectric breakdown, etc. © 2023 IEEE.

关键词： Continuous wave lasers

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Editorial:Special Topic on Federated Learning for IoT and Edge computing

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ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

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CoChain: high Concurrency Blockchain Sharding via Consensus on Consensus

CoChain: High Concurrency Blockchain Sharding via Consensus ...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Mingzhe Li You Lin Jin Zhang Wei Wang Computer Science and Engineering Department Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Computer Science and Engineering Department Hong Kong University of Science and Technology Technology and Research Institute of High Performance Computing and Agency for Science Singapore

Sharding is an effective technique to improve the scalability of blockchain. It splits nodes into multiple groups so that they can process transactions in parallel. To achieve higher parallelism and concurrency at large scales, it is desirable to maintain a large number of small shards. However, simply configuring small shards easily results in a higher fraction of malicious nodes inside shards, causing shard corruption and compromising system security. Existing sharding techniques hence demand large shards, at the expense of limited concurrency. To address this limitation, we propose CoChain: a blockchain sharding system that can securely configure small shards for enhanced concurrency. CoChain allows some shards to be corrupted. For security, each shard is monitored by multiple other shards. The latter reach a cross-shard Consensus on the Consensus results of their monitored shard. Once a corrupted shard is found, its subsequent consensus will be taken over by another shard, hence recovering the system. Via Consensus on Consensus, CoChain allows the existence of shards with more fraction of malicious nodes (<2/3) while securing the system, thus reducing the shard size safely. We implement CoChain based on Harmony and conduct extensive experiments. Compared with Harmony, CoChain achieves 35x throughput gain with 6,000+ nodes.

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DL-Chain: Scalable and Stable Blockchain Sharding with high Concurrency via Dual-Layer Consensus

arXiv

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arXiv 2024年

作者： Lin, You Li, Mingzhe Wei, Qingsong Liu, Yong Goh, Siow Mong Rick Zhang, Jin The Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong The Institute of High Performance Computing A*STAR Singapore Singapore The Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Sharding enhances blockchain scalability by partitioning nodes into multiple groups for concurrent transaction processing. Configuring a large number of small shards helps improve the transaction concurrency of a sharding system. However, it increases the fraction of malicious nodes within each shard, easily leading to shard corruption and jeopardizing system security. Some existing works have attempted to improve concurrency by reducing the shard size while maintaining security. However, they often require frequent and time-consuming recovery of corrupted shards, leading to severe system stagnation. Also, they usually require network-wide consensus to guarantee security, which limits scalability. To address these issues, we propose DL-Chain, a blockchain sharding system that can securely provide high concurrency with stable and scalable performance. Our core idea is a Dual-Layer architecture and consensus, which consists of numerous smaller proposer shards (PSs) for transaction processing and multiple larger finalizer committees (FCs) for transaction finalization. To avoid system stagnation and thus guarantee stable performance, we ensure PSs’ liveness even if they are corrupted through the cooperation of PSs and FCs, thus eliminating the recovery process of corrupted PSs. To better trade-off security and scalability, we fine-tune the FCs to enable multiple FCs to coexist securely. As a result, DL-Chain allows a larger fraction of malicious nodes in each PS ( Copyright © 2024, The Authors. All rights reserved.

关键词： Blockchain

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Privacy-Preserving 3D Skeleton-Based Video Action Recognition via Graph Convolution Network

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IEEE Transactions on Consumer Electronics 2024年

作者： Gao, Xuesong Li, Keqiu Liu, Xiulong Nie, Jie Chen, Weiqiang Tian, Yonghong Tianjin University College of Intelligence and Computing China Ocean University of China Faculty of Information Science and Engineering China Hisense Co. Ltd State Key Laboratory of Digital Multimedia Technology China Peking University School of Computer Science China

In recent years, 3D skeleton data-based human action recognition has attracted an increasing number of researchers because of its high robustness under illumination change and scene variation. However, in 3D skeleton data-based human action recognition, there are still some challenges including multi-object recognition and private data leakage. To address this issue, in this paper, we propose a privacy-preserving end-to-end action recognition model based on a Graph Convolutional Network (GCN). Specifically, we innovatively propose a 3D pose estimation method to obtain 3D human skeleton data from color data streams and depth data streams, in which the mean filtering technique is applied to protect users' video data privacy while not affecting the extraction of the skeleton data. Then, we propose a feature representation method and represent the skeleton data to a GCN model, which convolves the centroid of the human skeleton, four limbs, and the trunk of a person. After that, we propose a method to fuse and extract multiple persons' movement features and propose a novel GCN-based interactive action recognition model to recognize multiple people's skeleton data. Moreover, to protect recognition model privacy, we also design a ciphertext-based secure action recognition method, which guarantees the confidentiality of model parameters during action recognition. Finally, we evaluate the performance of our model in real datasets, and the results demonstrate that our model can achieve high recognition accuracy than existing models. Meanwhile, experimental results demonstrate that the mean filtering technique can well protect the privacy of the appearance data and the information of skeleton data can be well preserved. © 1975-2011 IEEE.

关键词： Differential privacy

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Participant attack on the deterministic measurement-device-independent quantum secret sharing protocol

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science China(Physics,Mechanics & Astronomy) 2021年第6期64卷 121-124页

作者： Yu-Guang Yang Yue-Chao Wang Yong-Li Yang Xiu-Bo Chen Dan Li Yi-Hua Zhou Wei-Min Shi Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboralory of Trusted Computing Beijing 100124China Guangxi Key Laboratory of Crptography and Information Security Guilin 541004China Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing 100876China College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China

In a recent article[Gao et al.,***.63,120311(2020)],a two-receiver measurement-deviceindependent quantum secret sharing(MDI-QSS)protocol was *** was proven to be secure against eavesdropping and generalized to the multireceiver ***,the participant attack is a fatal threat to QSS ***,we highlight that a dishonest participant can obtain a sender’s secret message alone without introducing any detectable error,evidencing the vulnerability of the MDI-QSS protocol to the participant attack.

关键词： quantum cryptography quantum secret sharing participant attack

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Enhancing Reentrancy Vulnerability Detection and Repair with a Hybrid Model Framework

Enhancing Reentrancy Vulnerability Detection and Repair with...

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Asia-Pacific Conference on Software Engineering

作者： Mengliang Li Xiaoxue Ren Han Fu Zhuo Li Jianling Sun State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security College of Computer Science and Technology Zhejiang University State Street Technology(Zhejiang) Ltd

ISBN: (数字)9798331534011

ISBN: (纸本)9798331534028

Smart contracts bring revolutionary changes to the credit landscape. However, their security remains intensely scrutinized due to numerous hacking incidents and inherent logical challenges. One well-known and representative issue is reentrancy vulnerability, exemplified by DAO attacks that lead to substantial economic losses. Conventional approaches to detecting and repairing reentrancy vulnerability often suffer from numerous limitations, including disregarding the intricate vulnerability features and the overfitting problems associated with imbalanced datasets. Large language models are distinguished for their excellent language understanding and have achieved explosive success in artificial intelligence. However, direct prompt-based LLMs-driven approaches for reentrancy vulnerability are plagued by inefficiencies and a lack of domain-specific vulnerability knowledge. This paper proposes a hybrid framework to enhance reentrancy vulnerability detection and repair and safeguard smart contract security. This unified framework comprises two crucial modules: enhanced DL-driven vulnerability detection and knowledge-aware LLMs-driven vulnerability repair. Our approach can significantly enhance reentrancy vulnerability detection and repair efficiency by integrating advanced techniques such as feature extraction, data balancing, deep learning networks, and knowledge-aware prompting. Extensive experimental results validate the superiority of our approach over state-of-the-art baselines, emphasizing its potential to fortify the security of smart contracts and blockchain-based systems. For instance, our approach can achieve 3.51 %, 2.31 %, 0.42%, and 0.85 % improvements in accuracy, recall, precision, and F1 score while detecting reentrancy vulnerability. Additionally, our approach also can achieve a 9.62% improvement in reentrancy vulnerability repair.

关键词： Deep learning Codes Large language models Smart contracts Maintenance engineering Syntactics Feature extraction Classification algorithms Security Software engineering

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Spatial-Temporal Reconstruction Error for AIGC-based Forgery Image Detection

Spatial-Temporal Reconstruction Error for AIGC-based Forgery...

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

作者： Chengji Shen Zhenjiang Liu Kaixuan Chen Jie Lei Mingli Song Zunlei Feng State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Idlefish Network Technology Alibaba Group College of Computer Science and Technology Zhejiang University of Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The remarkable success of AI-Generated Content (AIGC), especially diffusion image generation models, brings about unprecedented creative applications, but also creates fertile ground for malicious counterfeiting and crime. A highly effective family of forgery image detection methods based on diffusion reconstruction error has emerged, as images generated by diffusion are more easily reconstructed by any diffusion model. However, we find that existing methods only use reconstruction error from a single time step, failing to fully leverage the entire reconstruction process. To this end, we propose to comprehensively consider every single time step to form the Temporal Reconstruction Error (TRE) that offers a richer feature representation. Furthermore, we design temporal aggregation and spatial focusing modules from two dimensions respectively to more effectively extract discriminative information from the TRE feature. Finally, we validate the proposed method on two popular datasets, and experimental results demonstrate that the proposed approach achieves state-of-the-art performance.

关键词： Deepfakes Image synthesis Focusing Signal processing Feature extraction Diffusion models Forgery Data mining Speech processing Image reconstruction

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Quantum memory assisted entangled state verification with local measurements

arXiv

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arXiv 2023年

作者： Chen, Siyuan Xie, Wei Xu, Ping Wang, Kun Hefei National Research Center for Physical Sciences Microscale School of Physical Sciences University of Science and Technology of China Hefei230026 China College of Physics Jilin University Changchun130012 China School of Computer Science and Technology University of Science and Technology of China Hefei230027 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

We consider the quantum memory assisted quantum state verification task, where an adversary prepare independent multipartite entangled states and send to the local verifiers, who then store several copies in the quantum memory and measure them collectively to make decision. We establish an exact analytic formula for optimizing two-copy state verification, where the verifiers store two copies, and give a globally optimal two-copy strategy for multi-qubit graph states involving only Bell measurements. When the verifiers can store arbitrarily many copies, we present a dimension expansion technique that designs efficient verification strategies for this case, showcasing its application to efficiently verifying GHZ-like states. These strategies become increasingly advantageous with growing memory resources, ultimately approaching the theoretical limit of efficiency. Our findings demonstrate that quantum memories enhance state verification efficiency, sheding light on error-resistant strategies and practical applications of large-scale quantum memory-assisted verification. © 2023, CC BY.

关键词： Quantum entanglement

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SpatioTemporal Learning for Human Pose Estimation in Sparsely-Labeled Videos

arXiv

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arXiv 2025年

作者： Jiao, Yingying Wang, Zhigang Wu, Sifan Fan, Shaojing Liu, Zhenguang Xu, Zhuoyue Wu, Zheqi College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Zhejiang Gongshang University China School of Computing National University of Singapore Singapore The State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China

Human pose estimation in videos remains a challenge, largely due to the reliance on extensive manual annotation of large datasets, which is expensive and labor-intensive. Furthermore, existing approaches often struggle to capture long-range temporal dependencies and overlook the complementary relationship between temporal pose heatmaps and visual features. To address these limitations, we introduce STDPose, a novel framework that enhances human pose estimation by learning spatiotemporal dynamics in sparsely-labeled videos. STDPose incorporates two key innovations: 1) A novel Dynamic-Aware Mask to capture long-range motion context, allowing for a nuanced understanding of pose changes. 2) A system for encoding and aggregating spatiotemporal representations and motion dynamics to effectively model spatiotemporal relationships, improving the accuracy and robustness of pose estimation. STDPose establishes a new performance benchmark for both video pose propagation (i.e., propagating pose annotations from labeled frames to unlabeled frames) and pose estimation tasks, across three large-scale evaluation datasets. Additionally, utilizing pseudo-labels generated by pose propagation, STDPose achieves competitive performance with only 26.7% labeled data. Copyright © 2025, The Authors. All rights reserved.

关键词： Large datasets

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