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An Enhanced Clustering-Based (k, t)-Anonymity Algorithm for Graphs

Chinese Journal of Electronics 2025年第1期34卷 365-372页

作者： Yuanyuan Wang Xing Zhang Zhiguang Chu Wei Shi Xiang Li School of Electronics and Information Engineering Liaoning University of Technology Key Laboratory of Security for Network and Data in Industrial Internet of Liaoning Province Faculty of Information Technology Beijing University of Technology

As people become increasingly reliant on the Internet, securely storing and publishing private data has become an important issue. In real life, the release of graph data can lead to privacy breaches, which is a highly challenging problem. Although current research has addressed the issue of identity disclosure, there are still two challenges: First, the privacy protection for large-scale datasets is not yet comprehensive; Second, it is difficult to simultaneously protect the privacy of nodes, edges, and attributes in social networks. To address these issues, this paper proposes a(k,t)-graph anonymity algorithm based on enhanced clustering. The algorithm uses k-means++ clustering for k-anonymity and t-closeness to improve k-anonymity. We evaluate the privacy and efficiency of this method on two datasets and achieved good results. This research is of great significance for addressing the problem of privacy breaches that may arise from the publication of graph data.

关键词： Data privacy Uncertainty Social networking (online) Publishing Heuristic algorithms Scalability Clustering algorithms Privacy breach Internet protection

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Minimizing CNOT-count in quantum circuit of the extended Shor’s algorithm for ECDLP

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Cybersecurity 2025年第1期8卷 153-179页

作者： Xia Liu Huan Yang Li Yang State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina Beijing Youzhuju Network Technology Co. Ltd.BeijingChina

The elliptic curve discrete logarithm problem(ECDLP)is a popular choice for cryptosystems due to its high level of ***,with the advent of the extended Shor’s algorithm,there is concern that ECDLP may soon be *** the algorithm does ofer hope in solving ECDLP,it is still uncertain whether it can pose a real threat in *** the perspective of the quantum circuits of the algorithm,this paper analyzes the feasibility of cracking ECDLP using an ion trap quantum computer with improved quantum circuits for the extended Shor’s *** give precise quantum circuits for extended Shor’s algorithm to calculate discrete logarithms on elliptic curves over prime felds,including modular subtraction,three diferent modular multiplication,and modular ***,we incorporate and improve upon windowed arithmetic in the circuits to reduce the *** previous studies mostly focused on minimizing the number of qubits or the depth of the circuit,we focus on minimizing the number of CNOT gates in the circuit,which greatly afects the running time of the algorithm on an ion trap quantum ***,we begin by presenting implementations of basic arithmetic operations with the lowest known CNOT-counts,along with improved constructions for modular inverse,point addition,and windowed ***,we precisely estimate that,to execute the extended Shor’s algorithm with the improved circuits to factor an n-bit integer,the CNOT-count required is1237n^(3)/log n+2n^(2)+***,we analyze the running time and feasibility of the extended Shor’s algorithm on an ion trap quantum computer.

关键词： Elliptic curve discrete logarithm problem Extended Shor’s algorithm Quantum circuits Ion trap quantum computer

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Enabling Intelligent Connectivity: A Survey of Secure ISAC in 6G networks

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IEEE Communications Surveys and Tutorials 2025年第2期27卷 748-781页

作者： Zhu, Xiaoqiang Liu, Jiqiang Lu, Lingyun Zhang, Tao Qiu, Tie Wang, Chunpeng Liu, Yuan Beijing Jiaotong University School of Software Engineering Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Tianjin University College of Intelligence and Computing Tianjin300350 China Qilu University of Technology School of Cyber Security and the Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250353 China

The rapid growth of intelligent sensing capabilities and super computation power in 6G mobile communication systems has facilitated their application in diverse domains such as smart health, smart factories, and the industrial Internet of Things. Integrated Sensing and Communication (ISAC), as a core technology, has merged with artificial intelligence (AI) to enable intelligent connectivity, leading to a paradigm shift in traditional communication modes. This paper presents a visionary design for an ISAC-oriented unified IoT architecture that integrates software-defined communication and super-intelligent agents. By leveraging dynamic adaptability, self-learning, and optimization, the ISAC system can intelligently and flexibly respond to evolving requirements and environments. The architecture is redefined into three layers: the hardware layer, the omniscient layer, and the application layer. Furthermore, a retrospective survey of ISAC technology development over the past decade is conducted, highlighting new design principles for AI-empowered networks and multi-modals that support "intelligent connectivity" across various application scenarios and reinforce the security of ISAC. This paper categorizes the related works according to the different layer structures of the proposed architecture, and some important physical and machine learning models are introduced. Additionally, we summarize the current technological bottlenecks associated with ISAC and propose future research directions and potential solutions that lay the foundation for the future development of secure and intelligent communication networks. © 1998-2012 IEEE.

关键词： Computer architecture

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Iterative Bounded Distance Decoding With Random Flipping for Product-Like Codes

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IEEE Transactions on Communications 2025年第5期73卷 2864-2875页

作者： Li, Guorong Wang, Shiguo Zhao, Shancheng Jinan University National and Local Joint Research Center of Network Security Detection and Protection Technology College of Information Science and Technology Guangzhou510632 China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou Key Laboratory of Data Security and Privacy Preserving Guangzhou510632 China Changsha University of Science and Technology Computer and Communication Engineering Institute Changsha410114 China

Product-like codes are widely used in high-speed communication systems since they can be decoded with low-complexity hard decision decoders (HDDs). To meet the growing demand of data rates, enhanced HDDs are required. In this paper, we propose a novel soft-aided HDD (SA-HDD), termed iterative bounded distance decoding with random flipping (iBDD-RF), for product-like codes. In iBDD-RF, the soft reliability of a bit is a weighted sum of the output of bounded distance decoder (BDD) and the channel log-likelihood ratio (LLR). When the amplitude of the soft reliability of a bit is less than a given threshold, it is flipped with a given probability. This random flipping may make the decoder escape from the local optimum. To optimize the threshold and the flipping probability, we derive the density evolution (DE) equations of iBDD-RF for product codes (PCs) and staircase codes (SCs). Our extensive numerical results show that iBDD-RF outperforms iBDD with scaled reliability (iBDD-SR) over the binary-input additive white Gaussian noise (Bi-AWGN) channel. Particularly, for a PC with (255,239,2) Bose-Chaudhuri-Hocquenghem (BCH) code and an SC with (254,230,3) BCH code, iBDD-RF performs about 0.25 dB and 0.28 dB better than iBDD-SR, respectively. © 1972-2012 IEEE.

关键词： Optical fiber communication

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Fine-Grained Zero-Shot Object Detection via Semantic Decoupling 24

Fine-Grained Zero-Shot Object Detection via Semantic Decoupl...

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8th International Conference on Computer Science and Artificial Intelligence, CSAI 2024

作者： Duan, Lijuan Zhang, Zichen Gong, Zhi Beijing Key Laboratory of Trusted Computing Beijing University of Technology Beijing China School of Computer Science Beijing University of Technology Beijing China National Engineering Laboratory for Key Technologies of Information Security Level Protection Beijing China

ISBN: (纸本)9798400718182

Recent deep learning models have advanced object detection capabilities, but their reliance on labeled data limits scalability. Fine-grained zero-shot detection remains challenging due to the need to generalize to unseen classes and distinguish subtle features. To address this, we propose Fine-Grained Zero-Shot Object Detection via Semantic Decoupling (ZSD-SD). Our approach introduces a semantic decoupling module to separate visual features into semantic-related and semantic-unrelated components, facilitating alignment between visual and semantic spaces. A semantic-visual learning module further decouples these features, enhancing visual representations relevant to external semantic knowledge. This semantic-related representation significantly improves zero-shot performance. Experiments on two fine-grained object detection datasets demonstrate that ZSD-SD achieves state-of-the-art results. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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Spectrum fragmentation-aware dynamic network slicing deployment in computing power networks based on elastic optical networks

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Journal of Optical Communications 2025年第2期46卷 335-346页

作者： Wang, Laiming Zhang, Haojie Li, Lei Ren, Danping Hu, Jinhua Zhao, Jijun School of Information and Electrical Engineering Hebei University of Engineering Handan056038 China Hebei Key Laboratory of Security and Protection Information Sensing and Processing Handan056038 China Beijing KELIBANG Information Technology Corporation Limited Beijing10081 China

The widespread application of AI with high computing requirements has driven the rapid development of the computing field. Computing Power networks (CPNs) have been recognized as solutions to providing on-demand computing services, and its service provisioning can be modeled as a network slicing deployment problem. Elastic Optical networks (EONs) offer the flexibility to allocate spectrum resources, making them well-suited for network slicing technology. Consequently, EON-based CPNs have attracted considerable attention. However, the unbalanced distribution of computing resources leads to inefficient computing resource utilization. Meanwhile, spectrum resources may be isolated and difficult for other services. This phenomenon is known as spectrum fragmentation, leading to inefficient spectrum resource utilization. To achieve balanced and efficient resource utilization, this paper first analyzes the main reasons for load unbalance and spectrum fragmentation in CPNs: mismatched slicing deployment and inappropriate resource scheduling. Therefore, a dynamic network slicing scheme based on traffic prediction (DNS-TP) is designed. Its core highlight is cooperative optimization slicing deployment and resource scheduling based on spectrum fragmentation awareness. Simulation results show that the proposed scheme enhances the network slicing acceptance ratio, computing and spectrum resource utilization while exhibiting strong performance in resource balancing. © 2024 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Fiber optic networks

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Improving MPCitH with Preprocessing: Mask Is All You Need

IACR Transactions on Cryptographic Hardware and Embedded Sys...

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IACR Transactions on Cryptographic Hardware and Embedded Systems 2025年第2期2025卷 1-22页

作者： Liu, Guowei Liu, Guoxiao Jiang, Kaijie Yu, Qingyuan Jia, Keting Wei, Puwen Wang, Meiqin School of Cyber Science and Technology Shandong University Qingdao China Institute for Network Sciences and Cyberspace Tsinghua University Beijing China Institute for Advanced Study Tsinghua University Beijing China Quan Cheng Shandong Laboratory Jinan China Zhongguancun Laboratory Beijing China BNRist Tsinghua University Beijing China Key Laboratory of Cryptologic Technology and Information Security Ministry of Education Shandong University Jinan China

The MPC-in-the-head with preprocessing (MPCitH-PP) paradigm presents a novel approach for constructing post-quantum digital signatures like Picnic3. This paper revisits the MPCitH-PP construction, analyzing both its offline and online phases and proposing a reformulation of the protocol. By identifying redundant computations in these phases, we optimize them into a single phase, thereby enhancing the efficiency of MPCitH-PP. Furthermore, we explore the independence of the mask, demonstrating that it can be calculated in parallel, which also enables the optimization of the masked witness calculation. Our optimized implementation of Picnic3 shows significant improvements. At the L1 security level, the optimal software implementation reduces MPCitH-PP calculation time to about 30% of the previous implementation. The optimal signature implementation costs about 78% of the previous implementation time. At the L5 security level, MPCitH-PP with parallelism optimal is reduced to about 26% of the previous solution’s time, and the optimal signature implementation runs at about 53% of the previous solution’s time. For the hardware implementation, our optimizations reduce the clock cycles of MPCitH-PP from r sequential rounds to a single parallel round, where r denotes the number of rounds in the LowMC algorithm, with little change in hardware usage, and perform better in AT product, especially for parallel computing. © 2025, Ruhr-University of Bochum. All rights reserved.

关键词： Masks

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A circuit area optimization of MK-3 S-box

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Cybersecurity 2025年第1期8卷 61-71页

作者： Yanjun Li Weiguo Zhang Yiping Lin Jian Zou Jian Liu Information Industry Information Security Evaluation Center The 15th Research Institute of China Electronic Technology Group CorporationBeijing 100083China Henan Key Laboratory of Network Cryptography Technology Zhengzhou 450001China Beijing Electronic Science and Technology Institute No.7 Fufeng RoadFengtai DistricBeijing 100083China College of Computer and Data Science Fuzhou UniversityFuzhou 350108China

In MILCOM 2015,Kelly et *** the authentication encryption algorithm MK-3,which applied the 16-bit *** paper aims to implement the 16-bit S-box with less circuit ***,we classifed the irreducible polynomials over F_(2n)into three *** we compared the logic gates required for multiplication over the fnite feld constructed by the three types of irreducible *** to the comparison result,we constructed the composite felds, F_((2^(4))^(2))and F_((2^(8))^(2)).Based on the isomorphism of fnite felds,the operations over F_(2)^(16)can be conducted over F_((2^(8))^(2)).Similarly,elements over F28 can be mapped to the corresponding elements over F_((2^(4)))^(2).Next,the SAT solver was used to optimize the operations over smaller feld F_(2)^(4).At last,the architecture of the optimized MK-3 S-box was worked *** with the implementation proposed by the original designer,the circuit area of the MK-3 S-box in this paper is reduced by at least 55.9%.

关键词： irreducible operations optimization

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MMH-FE:AMulti-Precision and Multi-Sourced Heterogeneous Privacy-Preserving Neural network Training Based on Functional Encryption

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Computers, Materials & Continua 2025年第3期82卷 5387-5405页

作者： Hao Li Kuan Shao Xin Wang Mufeng Wang Zhenyong Zhang The State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model ***,dishonest clouds may infer user data,resulting in user data *** schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing *** address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training ***,we design a multi-precision functional encryption computation based on Euclidean ***,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced ***,we conduct experiments on three *** results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.

关键词： Functional encryption multi-sourced heterogeneous data privacy preservation neural networks

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APRIL: Towards Scalable and Transferable Autonomous Penetration Testing in Large Action Space via Action Embedding

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2443-2459页

作者： Zhou, Shicheng Liu, Jingju Lu, Yuliang Yang, Jiahai Hou, Dongdong Zhang, Yue Hu, Shulong National University of Defense Technology College of Electronic Engineering Hefei230037 China Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation Hefei230037 China Tsinghua University Institute for Network Sciences and Cyberspace Beijing National Research Center for Information Science and Technology Beijing100084 China National University of Defense Technology College of Computer Science and Technology Changsha410073 China

Penetration testing (pentesting) assesses cybersecurity through simulated attacks, while the conventional manual-based method is costly, time-consuming, and personnel-constrained. Reinforcement learning (RL) provides an agent-environment interaction learning paradigm, making it a promising way for autonomous pentesting. However, agents’ scalability in large action spaces and policy transferability across scenarios limit the applicability of RL-based autonomous pentesting. To address these challenges, we present a novel autonomous pentesting framework based on reinforcement learning (namely APRIL) to train agents that are scalable and transferable in large action spaces. In APRIL, we construct realistic, bounded, host-level state space via embedding techniques to avoid the complexities of dealing with unbounded network-level information. We employ semantic correlations between pentesting actions as prior knowledge to represent discrete action space into a continuous and semantically meaningful embedding space. Agents are then trained to reason over actions within the action embedding space, where two key methods are applied: an upper-confidence bound-based action refinement method to encourage efficient exploration, and a distance-aware loss to improve learning efficiency and generalization performance. We conduct experiments in simulated scenarios constructed based on virtualized vulnerable environments. The results demonstrate APRIL's scalability in large action spaces and its ability to facilitate policy transfer across diverse scenarios. © 2004-2012 IEEE.

关键词： Reinforcement learning

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