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IFAC-PapersOnLine 2021年第8期54卷 45-50页

作者： Zoltán Szabó József Bokor Péter Gáspár Systems and Control Laboratory Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) Budapest Hungary

Recently, system level approach has been proposed as a novel design tool for large-scale cyber physical systems in the discrete time LTI framework. The main idea of the new paradigm is a shift from constructing and parametrizing the desired optimal controller to the parametrization of the achievable closed loop behaviours (closed loop shape). Then, the controller is synthesised from the system level data in a way that keep the imposed structural constraints intact. In this paper we lift the main idea of the system level parametrization to a general level, that also includes the class of LPV systems. We also provide a general scheme for the realization related to different Möbius transforms which are formulated in terms of the original data and also keep intact the structural information.

关键词： system level approach Youla parametrization decentralized controller

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Differential Privacy for Class-based Data: A Practical Gaussian Mechanism

arXiv

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arXiv 2023年

作者： Ramakrishna, Raksha Scaglione, Anna Wu, Tong Ravi, Nikhil Peisert, Sean Division of Network and Systems Engineering EECS KTH Royal Institute of Technology Stockholm Sweden The Department of Electrical and Computer Engineering Cornell Tech New York CityNY10044 United States The Computer Sciences Research and Development Department Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

In this paper, we present a notion of differential privacy (DP) for data that comes from different classes. Here, the class-membership is private information that needs to be protected. The proposed method is an output perturbation mechanism that adds noise to the release of query response such that the analyst is unable to infer the underlying class-label. The proposed DP method is capable of not only protecting the privacy of class-based data but also meets quality metrics of accuracy and is computationally efficient and practical. We illustrate the efficacy of the proposed method empirically while outperforming the baseline additive Gaussian noise mechanism. We also examine a real-world application and apply the proposed DP method to the autoregression and moving average (ARMA) forecasting method, protecting the privacy of the underlying data source. Case studies on the real-world advanced metering infrastructure (AMI) measurements of household power consumption validate the excellent performance of the proposed DP method while also satisfying the accuracy of forecasted power consumption measurements. © 2023, CC BY.

关键词： Electric power utilization

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Efficient and Secure Spatial Range Query over Large-scale Encrypted Data

Efficient and Secure Spatial Range Query over Large-scale En...

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International Conference on Distributed Computing systems

作者： Yinbin Miao Chao Xu Yifeng Zheng Ximeng Liu Xiangdong Meng Robert H. Deng School of Cyber Engineering Xidian University Xi'an China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Mathematics and Computer Science Fuzhou University Fuzhou China Henan Key Laboratory of Network Cryptography Technology China School of Information Systems Singapore Management University Singapore

Spatial range query enjoys widespread application scenarios due to the ever-growing geo-positioning technology in recent years. Huge amounts of encrypted geo-location data are being outsourced to cloud servers to alleviate local storage and computational overheads without leaking sensitive information. However, most existing Privacy-preserving Spatial Range Query (PSRQ) cannot achieve high efficiency while satisfying strong security over large-scale encrypted spatial data. To strike a best possible balance between security and efficiency, we propose a novel efficient Privacy-preserving Spatial Range Query (eP-SRQ) scheme in dual-cloud architecture over large-scale dataset. Specifically, we propose an efficient PSRQ scheme by designing a novel index structure based on Geohash algorithm, Circular Shift and Coalesce Zero-Sum Garbled Bloom Filter (CSC-ZGBF) and Symmetric Homomorphic Encryption (SHE), which makes the computational complexity of query process independent of dataset size. Formal security analysis proves that our scheme can achieve Indistinguishability against Chosen-Plaintext Attack (IND-CPA), and extensive experiments prove that our scheme is feasible in real-world applications.

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SSRN

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SSRN 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nannig China Guangxi Key Laboratory of Multimedia Communications and Network Technology China China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China College of Systems Engineering National University of Defense Technology Changsha China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGA also uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a high-quality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data tra

关键词： Profitability

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Detecting Inconsistent Vulnerable Software Version in Security Vulnerability Reports 1

引用

4th International Conference on Frontiers in Cyber Security, FCS 2021

作者： Ren, Hansong Li, Xuejun Lei, Liao Ou, Guoliang Sun, Hongyu Wu, Gaofei Tian, Xiao Hu, Jinglu Zhang, Yuqing School of Cyber Engineering Xidian University Shaanxi Xian710126 China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guangxi Guilin541010 China National Computer Network Intrusion Prevention Center University of Chinese Academy of Sciences Beijing100049 China School of Information Production and Systems Waseda University Tokyo169-805 Japan College of Computer and Cyberspace Security Hainan University Haikou570228 China

ISBN: (数字)9789811905230

ISBN: (纸本)9789811905223

At present, the vulnerability database research has mainly focused on whether the disclosed information is accurate. However, the information differences between the various vulnerability databases have received little attention. This article proposes a WITTY (softWare versIon inconsisTency measuremenT sYstem) to detect the differences between the affected software versions of NVD and different language vulnerability databases (including English CVE, OpenWall, Chinese CNNVD, CNVD, and other eight databases). WITTY can enable Our large-scale quantitative information consistency. We introduce named entity recognition (NER) and relation extraction (RE) based on deep learning. We present custom design into named entity recognition (NER) and relation extraction (RE) based on deep learning, enabling WITTY to recognize previously invisible software names and versions based on sentence structure and context. Ground-truth shows that the system has a high accuracy rate (95.3% accuracy rate, 89.9% recall rate). We use data from 8 vulnerability databases in the past 21 years, involving 554,725 vulnerability reports. The results show that they are inconsistent. The software version is prevalent. The average exact match rate of English vulnerability databases CVE, OpenWall, and other vulnerability databases with cve is only 22.1%. The average exact match rate of Chinese CNNVD and CNVD is 49.5%, and the excat match rate of Russian vulnerability databases is 25.8%. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Database systems

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QoE-Guaranteed Optimization in MEC-Enabled Metaverse: An Active Inference Deep Reinforcement Learning Approach

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IEEE Transactions on Cognitive Communications and networking 2025年

作者： Du, Jianbo Gong, Jie Chu, Xiaoli Xiong, Zehui Chen, Xianfu Dong, Mianxiong Yu, F. Richard Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security School of Communications and Information Engineering Xi'an710121 China The University of Sheffield Mappin Street Department of Electronic and Electrical Engineering SheffieldS1 3JD United Kingdom Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore Singapore Shenzhen CyberAray Network Technology Company Ltd Shenzhen518000 China Muroran Institute of Technology Department of Sciences and Informatics Muroran050-8585 Japan Carleton University Systems and Computer Engineering OttawaON Canada

In this paper, we consider a MEC-enabled metaverse scenario which consists of a remote metaverse server and an edge server that cooperates to provide services to mobile users. The edge server is deployed at the base station (BS), serves a dual role: augmenting computational capabilities for user equipment (UE) and pre-caching a portion of the metaverse service contents before each time slot. Moreover, the foreground information and the requested contents generated by the UEs can also be cached to the BS. We formulate a problem to maximize the cache hit number by jointly optimizing contents pre-caching and resource allocation at the BS while considering UEs preference and reducing the UEs total energy consumption, essential for the efficient delivery of services in dynamic MEC environments. To solve this problem, we reformulate it as a partially observable markov decision process and propose an active inference enabled deep reinforcement learning algorithm, which combines active inference with deep reinforcement learning to select the optimal strategy by minimizing the expected free energy. Simulations show that the proposed algorithm can effectively improve the total quality of experience and the cache hit number of UEs, while minimizing the UEs total energy consumption compared with other baseline algorithms © 2015 IEEE.

关键词： Active learning

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Safety Guaranteed Power-Delivered-to-Load Maximization for Magnetic Wireless Power Transfer

Safety Guaranteed Power-Delivered-to-Load Maximization for M...

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

作者： Wangqiu Zhou Xinyu Wang Hao Zhou Shenyao Jiang Zhi Liu Yusheng Ji LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Electromagnetic radiation (EMR) safety has always been a critical reason for hindering the development of magneticenabled wireless power transfer technology. People focus on the actual received energy at charging devices while paying attention to their health. Thus, we study this significant problem in this paper, and propose a universal safety guaranteed power-delivered-to-load (PDL) maximization scheme (called SafeGuard). Technically, we first utilize the off-the-shelf electromagnetic simulator to perform the EMR distribution analysis to ensure the universality of the method. Then, we innovatively introduce the concept of multiple importance sampling for achieving efficient EMR safety constraint extraction. Finally, we treat the proposed optimization problem as an optimal boundary point search problem from the perspective of space geometry, and devise a brand-new grid-based multi-constraint parallel processing algorithm to efficiently solve it. We implement a system prototype for SafeGuard, and conduct extensive experiments to evaluate it. The results indicate that our SafeGuard can obviously improve the achieved PDL by up to 1.75× compared with the state-of-the-art baseline while guaranteeing EMR safety. Furthermore, SafeGuard can accelerate the solution process by 29.12× compared with the traditional numerical method to satisfy the fast optimization requirement of wireless charging systems.

关键词： Performance evaluation Monte Carlo methods System performance Inductive charging Prototypes Wireless power transfer Parallel processing

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MPC Control Strategy for Autonomous Vehicles Driving in Roundabouts

MPC Control Strategy for Autonomous Vehicles Driving in Roun...

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Mediterranean Conference on Control and Automation (MED)

作者： Zsó fia Farkas Andrá s Mihá ly Pé ter Gá spá r Systems and Control Laboratory Institute for Computer Science and Control (SZTAKI) E&#x00F6 tv&#x00F6 s Lor&#x00E1 nd Research Network (ELKH) Budapest Hungary Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Budapest Hungary

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

The appearance of autonomous vehicles (AVs) in transportation has increased the attention of the scientific community to develop modern solutions for the control design of AVs in different traffic scenarios. In this paper a control method is proposed for the coordination of autonomous vehicles in roundabout scenarios. For collision avoidance and minimization of traveling time, a Model Predictive Control (MPC) with a centralized controller is introduced to calculate the traveling times of the vehicles. A presented algorithm determines velocity profiles for safety reasons and for the reduction of possible congestion. The operation of the proposed MPC method is tested and demonstrated in CarSim simulation environment.

关键词： Automation Control design Transportation Prediction algorithms Minimization Safety Collision avoidance

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SRIF: Data-Free Knowledge Distillation via Stable Regulation and Input Filtering

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Zherui Xu, Rongtao Wang, Changwei Xu, Shibiao Zhou, Jie Xu, Wenhao Gao, Longxiang Xu, Wenbo Guo, Li Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China Qilu University of Technology Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China

Data-free knowledge distillation (DFKD) enables knowledge transfer from a pre-trained teacher to a student network without accessing the real dataset. However, generator-based DFKD methods struggle to ensure that the synthetic images accurately reflect the real dataset distribution. The update of the generator network relies heavily on teacher category guidance, but varying teacher prediction accuracy across categories leads to inconsistent synthetic image quality. Such variations introduce a distribution shift between synthetic and real datasets, negatively impacting student network performance during knowledge distillation. To address this challenge, we propose the SRIF, comprising two components: Student-Driven Flexible Filtering (SDFF) and Re-weighting for Independent Regularization (RIR). SDFF filters out synthetic images affected by the category distribution shift during data generation, producing a more reliable dataset. RIR, applied during distillation, encourages the student to learn stable causal relationships through sample reweighting. Both components flexibly integrate into existing DFKD frameworks, improving performance while reducing training costs. © 1999-2012 IEEE.

关键词： Data-Free Knowledge Distillation Distribution Shift Stable Prediction

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Joint Secure Beamforming for RIS-Aided Integrated Multibeam-Satellite Terrestrial networks

Joint Secure Beamforming for RIS-Aided Integrated Multibeam-...

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Future Communications and networks (FCN), International Conference on

作者： Yu Zhang Yang Yang Shuangrui Zhao Teng Li Yulong Shen Xiaohong Jiang Jianfeng Ma School of Computer Science and Technology Xidian University Xi’an China Shaanxi Key Laboratory of Network and System Security Xidian University Xi’an China School of Cyber Engineering Xidian University Xi’an China School of Systems Information Science Future University Hakodate Japan

ISBN: (数字)9798350396034

ISBN: (纸本)9798350396041

This paper focuses on a reconfigurable intelligent surface (RIS) enabled integrated multibeam-satellite terrestrial network, which consists of a satellite, a base station (BS), an RIS, multiple earth stations (ESs), an eavesdropper (Eve) and a terrestrial user (TU). In such a network, the satellite provides broadband services to multiple ESs, while the BS transmits friendly interference to the Eve for crippling confidential information interception with the help of the RIS. To enhance the security performance, we provide a theoretical modelling secrecy rate (SR) and formulate the SR maximization problem subject to the signal-to-interference-plus-noise ratio (SINR) requirements, the total transmit power limit and the RIS unitmodulus constraints. By combining approaches like alternative optimization (AO), bisection search, semi-definite programming (SDP), eigenvalue decomposition, and Gaussian randomization, we then propose a joint secure beamforming scheme to solve the non-convex optimization problem and thus identify the optimal beamforming designs for the satellite, the BS, and the RIS. Simulation results indicate the effectiveness of security enhancement via the proposed joint secure beamforming scheme.

关键词： Satellites Array signal processing Simulation Interference Reconfigurable intelligent surfaces Space-air-ground integrated networks Search problems

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