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Batch Secret Sharing for Secure Multi-party Computation in Asynchronous Network 认领 引用
1
作者 黄征 龚征 李强 《Journal of Shanghai Jiaotong university(Science)》 EI 2009年第1期112-116,共5页
This paper proposes an efficient batch secret sharing protocol among n players resilient to t 〈 n/4 players in asynchronous network. The construction of our protocol is along the line of Hirt＇s protocol which works ... This paper proposes an efficient batch secret sharing protocol among n players resilient to t 〈 n/4 players in asynchronous network. The construction of our protocol is along the line of Hirt＇s protocol which works in synchronous model. Compared with the method of using secret share protocol m times to share m secrets, our protocol is quite efficient. The protocol can be used to improve the efficiency of secure multi-party computation （MPC） greatly in asynchronous network. 展开更多
关键词 secret share secure multi-party computation asynchronous network
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Quantum Secure Multiparty Computation:Bridging Privacy,Security,and Scalability in the Post-Quantum Era 认领 引用
2
作者 Sghaier Guizani Tehseen Mazhar Habib Hamam 《Computers, Materials & Continua》 SCIE EI 2026年第4期1-25,共25页
The advent of quantum computing poses a significant challenge to traditional cryptographic protocols,particularly those used in SecureMultiparty Computation(MPC),a fundamental cryptographic primitive for privacypreser... The advent of quantum computing poses a significant challenge to traditional cryptographic protocols,particularly those used in SecureMultiparty Computation(MPC),a fundamental cryptographic primitive for privacypreserving computation.Classical MPC relies on cryptographic techniques such as homomorphic encryption,secret sharing,and oblivious transfer,which may become vulnerable in the post-quantum era due to the computational power of quantum adversaries.This study presents a review of 140 peer-reviewed articles published between 2000 and 2025 that used different databases like MDPI,IEEE Explore,Springer,and Elsevier,examining the applications,types,and security issues with the solution of Quantum computing in different fields.This review explores the impact of quantum computing on MPC security,assesses emerging quantum-resistant MPC protocols,and examines hybrid classicalquantum approaches aimed at mitigating quantum threats.We analyze the role of Quantum Key Distribution(QKD),post-quantum cryptography(PQC),and quantum homomorphic encryption in securing multiparty computations.Additionally,we discuss the challenges of scalability,computational efficiency,and practical deployment of quantumsecure MPC frameworks in real-world applications such as privacy-preserving AI,secure blockchain transactions,and confidential data analysis.This review provides insights into the future research directions and open challenges in ensuring secure,scalable,and quantum-resistant multiparty computation. 展开更多
关键词 Quantum computing secure multiparty computation(MPC) post-quantum cryptography(PQC) quantum key distribution(QKD) privacy-preserving computation quantum homomorphic encryption quantum network security federated learning blockchain security quantum cryptography
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Concretely ecient secure multi-party computation protocols:survey and mor 认领 引用 被引量：6
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作者 Dengguo Feng Kang Yang 《Security and Safety》 2022年第1期47-89,共43页
Secure multi-party computation(MPC)allows a set of parties to jointly compute a function on their private inputs,and reveals nothing but the output of the function.In the last decade,MPC has rapidly moved from a purel... Secure multi-party computation(MPC)allows a set of parties to jointly compute a function on their private inputs,and reveals nothing but the output of the function.In the last decade,MPC has rapidly moved from a purely theoretical study to an object of practical interest,with a growing interest in practical applications such as privacy-preserving machine learning(PPML).In this paper,we comprehensively survey existing work on concretely ecient MPC protocols with both semi-honest and malicious security,in both dishonest-majority and honest-majority settings.We focus on considering the notion of security with abort,meaning that corrupted parties could prevent honest parties from receiving output after they receive output.We present high-level ideas of the basic and key approaches for designing di erent styles of MPC protocols and the crucial building blocks of MPC.For MPC applications,we compare the known PPML protocols built on MPC,and describe the eciency of private inference and training for the state-of-the-art PPML protocols.Further-more,we summarize several challenges and open problems to break though the eciency of MPC protocols as well as some interesting future work that is worth being addressed.This survey aims to provide the recent development and key approaches of MPC to researchers,who are interested in knowing,improving,and applying concretely ecient MPC protocols. 展开更多
关键词 Secure multi-party computation Privacy-preserving machine learning Secret sharings Garbled circuits Oblivious transfer and its arithmetic generalization
Robust peer-to-peer learning via secure multi-party computation 认领 引用 被引量：1
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作者 Yongkang Luo Wenjian Luo +2 位作者 Ruizhuo Zhang Hongwei Zhang Yuhui Shi 《Journal of Information and Intelligence》 2023年第4期341-351,共11页
To solve the data island problem,federated learning(FL)provides a solution paradigm where each client sends the model parameters but not the data to a server for model aggregation.Peer-to-peer(P2P)federated learning f... To solve the data island problem,federated learning(FL)provides a solution paradigm where each client sends the model parameters but not the data to a server for model aggregation.Peer-to-peer(P2P)federated learning further improves the robustness of the system,in which there is no server and each client communicates directly with the other.For secure aggregation,secure multi-party computing(SMPC)protocols have been utilized in peer-to-peer manner.However,the ideal SMPC protocols could fail when some clients drop out.In this paper,we propose a robust peer-to-peer learning(RP2PL)algorithm via SMPC to resist clients dropping out.We improve the segmentbased SMPC protocol by adding a check and designing the generation method of random segments.In RP2PL,each client aggregates their models by the improved robust secure multi-part computation protocol when finishes the local training.Experimental results demonstrate that the RP2PL paradigm can mitigate clients dropping out with no significant degradation in performance. 展开更多
关键词 Federated learning Swarm learning Secure multi-party computation Peer-to-peer learning
Nearly universal and efficient quantum secure multi-party computation protocol 认领 引用
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作者 Han Yushan Che Bichen +2 位作者 Liu Jiali Dou Zhao Di Junyu 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2022年第4期51-68,88,共18页
Universality is an important property in software and hardware design.This paper concentrates on the universality of quantum secure multi-party computation(SMC)protocol.First of all,an in-depth study of universality h... Universality is an important property in software and hardware design.This paper concentrates on the universality of quantum secure multi-party computation(SMC)protocol.First of all,an in-depth study of universality has been conducted,and then a nearly universal protocol is proposed by using the Greenberger-Horne-Zeilinger(GHZ)-like state and stabilizer formalism.The protocol can resolve the quantum SMC problem which can be deduced as modulo subtraction,and the steps are simple and effective.Secondly,three quantum SMC protocols based on the proposed universal protocol:Quantum private comparison(QPC)protocol,quantum millionaire(QM)protocol,and quantum multi-party summation(QMS)protocol are presented.These protocols are given as examples to explain universality.Thirdly,analyses of the example protocols are shown.Concretely,the correctness,fairness,and efficiency are confirmed.And the proposed universal protocol meets security from the perspective of preventing inside attacks and outside attacks.Finally,the experimental results of the example protocols on the International Business Machines(IBM)quantum platform are consistent with the theoretical results.Our research indicates that our protocol is universal to a certain degree and easy to perform. 展开更多
关键词 universality quantum secure multi-party computation security Greenberger-Horne-Zeilinger-like state simple operation
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On Privacy-Preserved Machine Learning Using Secure Multi-Party Computing:Techniques and Trends 认领 引用
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作者 Oshan Mudannayake Amila Indika +2 位作者 Upul Jayasinghe Gyu MyoungLee Janaka Alawatugoda 《Computers, Materials & Continua》 SCIE EI 2025年第11期2527-2578,共52页
The rapid adoption of machine learning in sensitive domains,such as healthcare,finance,and government services,has heightened the need for robust,privacy-preserving techniques.Traditional machine learning approaches l... The rapid adoption of machine learning in sensitive domains,such as healthcare,finance,and government services,has heightened the need for robust,privacy-preserving techniques.Traditional machine learning approaches lack built-in privacy mechanisms,exposing sensitive data to risks,which motivates the development of Privacy-Preserving Machine Learning(PPML)methods.Despite significant advances in PPML,a comprehensive and focused exploration of Secure Multi-Party Computing(SMPC)within this context remains underdeveloped.This review aims to bridge this knowledge gap by systematically analyzing the role of SMPC in PPML,offering a structured overviewof current techniques,challenges,and future directions.Using a semi-systematicmapping studymethodology,this paper surveys recent literature spanning SMPC protocols,PPML frameworks,implementation approaches,threat models,and performance metrics.Emphasis is placed on identifying trends,technical limitations,and comparative strengths of leading SMPC-based methods.Our findings reveal thatwhile SMPCoffers strong cryptographic guarantees for privacy,challenges such as computational overhead,communication costs,and scalability persist.The paper also discusses critical vulnerabilities,practical deployment issues,and variations in protocol efficiency across use cases. 展开更多
关键词 Cryptography data privacy machine learning multi-party computation privacy SMPC PPML
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A Secure Task Offloading Scheme for UAV-Assisted MEC with Dynamic User Clustering and Cooperative Jamming: A Method Combining K-Means and SAC (K-SAC) 认领 引用
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作者 Jiajia Liu Shuchen Pang +7 位作者 Peng Xie Haitao Zhou Chenxi Du Haoran Hu Bo Tang Jianhua Liu Fei Jia Huibing Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第6期1323-1346,共24页
In the unmanned aerial vehicle(UAV)assisted edge computing system,the broadcast characteristics of the UAV signal,the high mobility of the UAV,and the limited airborne energy make the task offloading strategy face cha... In the unmanned aerial vehicle(UAV)assisted edge computing system,the broadcast characteristics of the UAV signal,the high mobility of the UAV,and the limited airborne energy make the task offloading strategy face challenges such as increased risk of information disclosure,limited computing resources,and the trade-off between energy consumption and flight time.To address these issues,we propose a K-means in-depth reinforcement learning algorithm based on Soft Actor-Critic(SAC).The proposed method first leverages the K-means clustering algorithm to determine the optimal deployment of ground jammers based on the final distribution of mobile users.Then,building upon the SAC framework,the Cross-Entropy Method(CEM)global sampling strategy is incorporated into the action output phase to form the K-SAC algorithm.This algorithm aims to maximize system rewards,which holistically balance task offloading delay,energy consumption,and secure offloading rate.Consequently,it jointly optimizes the optimal hovering positions of auxiliary UAVs and the task offloading ratio for each user,leading to an overall performance improvement in system security and efficiency.Finally,compared with current schemes,the system benefits achieved by the proposed scheme were 9.83%higher on average in different computing task sizes,13.67%higher on average in different task complexities,and 14.63%higher on average in different interference powers. 展开更多
关键词 Mobile edge computing(MEC) SAC communication security K-means UAV
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Preserving Privacy of Software-Defined Networking Policies by Secure Multi-Party Computation 认领 引用 被引量：1
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作者 Maryam Zarezadeh Hamid Mala Homa Khajeh 《Journal of Computer Science & Technology》 SCIE EI CSCD 2020年第4期863-874,共12页
In software-defined networking(SDN),controllers are sinks of information such as network topology collected from switches.Organizations often like to protect their internal network topology and keep their network poli... In software-defined networking(SDN),controllers are sinks of information such as network topology collected from switches.Organizations often like to protect their internal network topology and keep their network policies private.We borrow techniques from secure multi-party computation(SMC)to preserve the privacy of policies of SDN controllers about status of routers.On the other hand,the number of controllers is one of the most important concerns in scalability of SMC application in SDNs.To address this issue,we formulate an optimization problem to minimize the number of SDN controllers while considering their reliability in SMC operations.We use Non-Dominated Sorting Genetic Algorithm II(NSGA-II)to determine the optimal number of controllers,and simulate SMC for typical SDNs with this number of controllers.Simulation results show that applying the SMC technique to preserve the privacy of organization policies causes only a little delay in SDNs,which is completely justifiable by the privacy obtained. 展开更多
关键词 software-defined networking (SDN) privacy secure multi-party computation (SMC) structure function multi-objective optimization
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A Comprehensive Survey on Blockchain-Enabled Techniques and Federated Learning for Secure 5G/6G Networks:Challenges,Opportunities,and Future Directions 认领 引用
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作者 Muhammad Asim Abdelhamied A.Ateya +4 位作者 Mudasir Ahmad Wani Gauhar Ali Mohammed ElAffendi Ahmed A.Abd El-Latif Reshma Siyal 《Computers, Materials & Continua》 SCIE EI 2026年第3期117-161,共45页
The growing developments in 5G and 6G wireless communications have revolutionized communications technologies,providing faster speeds with reduced latency and improved connectivity to users.However,it raises significa... The growing developments in 5G and 6G wireless communications have revolutionized communications technologies,providing faster speeds with reduced latency and improved connectivity to users.However,it raises significant security challenges,including impersonation threats,data manipulation,distributed denial of service(DDoS)attacks,and privacy breaches.Traditional security measures are inadequate due to the decentralized and dynamic nature of next-generation networks.This survey provides a comprehensive review of how Federated Learning(FL),Blockchain,and Digital Twin(DT)technologies can collectively enhance the security of 5G and 6G systems.Blockchain offers decentralized,immutable,and transparent mechanisms for securing network transactions,while FL enables privacy-preserving collaborative learning without sharing raw data.Digital Twins create virtual replicas of network components,enabling real-time monitoring,anomaly detection,and predictive threat analysis.The survey examines major security issues in emerging wireless architectures and analyzes recent advancements that integrate FL,Blockchain,and DT to mitigate these threats.Additionally,it presents practical use cases,synthesizes key lessons learned,and identifies ongoing research challenges.Finally,the survey outlines future research directions to support the development of scalable,intelligent,and robust security frameworks for next-generation wireless networks. 展开更多
关键词 5G/6G blockchain federated learning edge computing security
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Security and privacy in edge computing:a survey of electric vehicles 认领 引用
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作者 Honghao Gao Wanqiu Huang +1 位作者 Yueshen Xu Youhuizi Li 《Digital Communications and Networks》 SCIE EI CSCD 2026年第2期223-235,共13页
Electric Vehicles(EVs)have developed into a complex ecosystem that includes many technical components such as task offloading on mobile devices,the Internet of Vehicles(IoV),and smart grids.Moreover,Edge Computing(EC)... Electric Vehicles(EVs)have developed into a complex ecosystem that includes many technical components such as task offloading on mobile devices,the Internet of Vehicles(IoV),and smart grids.Moreover,Edge Computing(EC)is a technique that relocates applications and services closer to end-users.This computing paradigm has been extensively adopted across many scenarios,effectively reducing the load on the cloud computing infrastructure and centralized server facilities.EVs are closely related to EC in many aspects since electric vehicles are typically supported by modern communication and Artificial Intelligence(AI)technologies,such as,sensor networks,computation offloading,autonomous systems,and blockchain.However,the diversity and heterogeneity of edge devices have raised many security and privacy concerns in electric vehicles,and some complex EC scenarios make addressing these issues even more challenging.In this paper,we provide a comprehensive review of the security and privacy concerns raised by EC in EVs.First,we elaborate on the development,characteristics,and applications of EC in EVs.Next,we describe the typical architectures used to ensure the security and privacy of EC in EVs.Then,we analyze the risks and challenges related to the security and privacy of EC in EVs,focusing on several significant scenarios(e.g.,offloading,the IoV,and smart grids).We also discuss current research progress on the security and privacy,covering methodologies,architectures,algorithms,insights,and performance.Finally,we discuss several future challenges and issues regarding the security and privacy of EC in EVs. 展开更多
关键词 Electric vehicle Edge computing Security and privacy Internet of vehicle Smart grid
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Physical-layer secure hybrid task scheduling and resource management for fog computing IoT networks 认领 引用 被引量：1
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作者 ZHANG Shibo GAO Hongyuan +1 位作者 SU Yumeng SUN Rongchen 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第5期1146-1160,共15页
Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems... Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems are susceptible to malicious eavesdropping attacks during the information transmission,and this issue has not been adequately addressed.In this paper,we propose a physical-layer secure fog computing IoT system model,which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers.The secrecy rate of the proposed model is analyzed,and the quantum galaxy–based search algorithm(QGSA)is proposed to solve the hybrid task scheduling and resource management problem of the network.The computational complexity and convergence of the proposed algorithm are analyzed.Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks.Moreover,the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios. 展开更多
关键词 fog computing Internet-of-Things(IoT) physical layer security hybrid task scheduling and resource management quantum galaxy-based search algorithm(QGSA)
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A secure mist-fog-assisted cooperative offloading framework for sustainable smart city development 认领 引用 被引量：1
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作者 Subhranshu Sekhar Tripathy Sujit Bebortta +3 位作者 Mazin Abed Mohammed Muhammet Deveci Haydar Abdulameer Marhoon Radek Martinek 《Digital Communications and Networks》 SCIE EI CSCD 2026年第1期165-179,共15页
Practical applications of smart cities and the Internet of Things(IoT)have multiplied,posing many difficulties in network performance,dependability,and security.Concerns of accessibility,reliability,sustainability,and... Practical applications of smart cities and the Internet of Things(IoT)have multiplied,posing many difficulties in network performance,dependability,and security.Concerns of accessibility,reliability,sustainability,and security too have arisen correspondingly because of the decentralized character of the smart city and IoT systems.Fog computing offers a foundation for various applications,including cognitive support,health and social services,intelligent transportation systems,and pervasive computing and communications.Fog computing can help enhance these apps'productivity and lower the end-to-end delay experienced by such time-sensitive applications.In this research,we propose a reliable and secure service delivery strategy at the network edge for smart cities.To improve the availability and dependability,along with the security of smart city applications,the approach employs a combined method uniting distributed fog servers in addition to mist servers with the help of an intrusion detection system.Simulation findings suggest a reduction of 40.3%in the delay incurred by each service request for highly dense areas and 60.6%for moderately dense environments.Furthermore,the system has low false-negative rates and high detection and accuracy rates,decreasing service requests 2%. 展开更多
关键词 Security Reliability Trustworthy computing Resource provisioning Threat detection Fog computing Internet of things
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An Adaptive Intrusion Detection Framework for IoT: Balancing Accuracy and Computational Efficiency 认领 引用
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作者 Abdulaziz A.Alsulami Badraddin Alturki +2 位作者 Ahmad J.Tayeb Rayan A.Alsemmeari Raed Alsini 《Computers, Materials & Continua》 SCIE EI 2026年第6期1214-1241,共28页
Intrusion Detection Systems(IDS)play a critical role in protecting networked environments from cyberattacks.They have become increasingly important in smart environments such as the Internet of Things(IoT)systems.Howe... Intrusion Detection Systems(IDS)play a critical role in protecting networked environments from cyberattacks.They have become increasingly important in smart environments such as the Internet of Things(IoT)systems.However,IDS for IoT networks face critical challenges due to hardware constraints,including limited computational resources and storage capacity,which lead to high feature dimensionality,prediction uncertainty,and increased processing cost.These factors make many conventional detection approaches unsuitable for real-time IoT deployment.To address these challenges,this paper proposes an adaptive intrusion detection framework that intelligently balances detection accuracy and computational efficiency.The proposed framework integrates mutual information(MI)feature selection model,deep contextual embeddings,and an adaptive decision mechanism.The MI model identifies and retains the most informative features,which reduces dimensionality while maintaining high detection accuracy.The adaptive decision dynamically selects between multiple inference paths to ensure that additional computation is needed only when the uncertainty level is high.Experimental evaluations on benchmark IoT datasets namely RT-IoT-2022,CIC-IoT-2023 and CIC-IoMT-2024 show that the proposed framework achieves F1-score of 99.92%,96.66%,and 99.84%,respectively,with an average inference time of approximately 0.105 ms per sample.These results demonstrate that the framework effectively adapts inference complexity to data uncertainty,which provides an intelligent,interpretable and efficient solution for real-world IoT intrusion detection. 展开更多
关键词 Adaptive IDS cyberattack detection computational cost IoT security
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Computational Optimization of RIS-Enhanced Backscatter and Direct Communication for 6G IoT:A DDPG-Based Approach with Physical Layer Security 认领 引用 被引量：2
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作者 Syed Zain Ul Abideen Mian Muhammad Kamal +4 位作者 Eaman Alharbi Ashfaq Ahmad Malik Wadee Alhalabi Muhammad Shahid Anwar Liaqat Ali 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第3期2191-2210,共20页
The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyeffic... The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyefficient communication.This study explores the integration of Reconfigurable Intelligent Surfaces(RIS)into IoT networks to enhance communication performance.Unlike traditional passive reflector-based approaches,RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes,addressing critical IoT challenges such as energy efficiency,limited communication range,and double-fading effects in backscatter communication.We propose a novel computational framework that combines RIS functionality with Physical Layer Security(PLS)mechanisms,optimized through the algorithm known as Deep Deterministic Policy Gradient(DDPG).This framework adaptively adapts RIS configurations and transmitter beamforming to reduce key challenges,including imperfect channel state information(CSI)and hardware limitations like quantized RIS phase shifts.By optimizing both RIS settings and beamforming in real-time,our approach outperforms traditional methods by significantly increasing secrecy rates,improving spectral efficiency,and enhancing energy efficiency.Notably,this framework adapts more effectively to the dynamic nature of wireless channels compared to conventional optimization techniques,providing scalable solutions for large-scale RIS deployments.Our results demonstrate substantial improvements in communication performance setting a new benchmark for secure,efficient and scalable 6G communication.This work offers valuable insights for the future of IoT networks,with a focus on computational optimization,high spectral efficiency and energy-aware operations. 展开更多
关键词 Computational optimization reconfigurable intelligent surfaces(RIS) 6G networks IoT and DDPG physical layer security(PLS) backscatter communication
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EG-STC: An Efficient Secure Two-Party Computation Scheme Based on Embedded GPU for Artificial Intelligence Systems 认领 引用
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作者 Zhenjiang Dong Xin Ge +2 位作者 Yuehua Huang Jiankuo Dong Jiang Xu 《Computers, Materials & Continua》 SCIE EI 2024年第6期4021-4044,共24页
This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of connectivity.W... This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of connectivity.We delve into the emerging trend of machine learning on embedded devices,enabling tasks in resource-limited environ-ments.However,the widespread adoption of machine learning raises significant privacy concerns,necessitating the development of privacy-preserving techniques.One such technique,secure multi-party computation(MPC),allows collaborative computations without exposing private inputs.Despite its potential,complex protocols and communication interactions hinder performance,especially on resource-constrained devices.Efforts to enhance efficiency have been made,but scalability remains a challenge.Given the success of GPUs in deep learning,lever-aging embedded GPUs,such as those offered by NVIDIA,emerges as a promising solution.Therefore,we propose an Embedded GPU-based Secure Two-party Computation(EG-STC)framework for Artificial Intelligence(AI)systems.To the best of our knowledge,this work represents the first endeavor to fully implement machine learning model training based on secure two-party computing on the Embedded GPU platform.Our experimental results demonstrate the effectiveness of EG-STC.On an embedded GPU with a power draw of 5 W,our implementation achieved a secure two-party matrix multiplication throughput of 5881.5 kilo-operations per millisecond(kops/ms),with an energy efficiency ratio of 1176.3 kops/ms/W.Furthermore,leveraging our EG-STC framework,we achieved an overall time acceleration ratio of 5–6 times compared to solutions running on server-grade CPUs.Our solution also exhibited a reduced runtime,requiring only 60%to 70%of the runtime of previously best-known methods on the same platform.In summary,our research contributes to the advancement of secure and efficient machine learning implementations on resource-constrained embedded devices,paving the way for broader adoption of AI technologies in various applications. 展开更多
关键词 Secure two-party computation embedded GPU acceleration privacy-preserving machine learning edge computing
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Cost-Aware Secure Offloading Based on Edge-Edge Collaboration in SDN-Enabled MEC Networks with Deep Reinforcement Learning 认领 引用
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作者 Nie Xuefang Chen Xingbang +2 位作者 Zhou Tianqing Zhou Qiangqiang Zhang Jiliang 《China Communications》 SCIE EI CSCD 2026年第6期216-231,共16页
Mobile edge computing(MEC)has been envisioned as an important technique of the 5th generation(5G)wireless networks to handle computation tasks from mobile terminal devices(TDs).To accommodate the low-delay requirement... Mobile edge computing(MEC)has been envisioned as an important technique of the 5th generation(5G)wireless networks to handle computation tasks from mobile terminal devices(TDs).To accommodate the low-delay requirements of mobile TDs with latency-sensitive and computation-intensive applications,we propose an edge-edge collaborative paradigm for software-defined networking(SDN)-enabled MEC networks by merging adjacent MEC servers with redundant computing resources to execute offloading tasks.Meanwhile,since task data distributed among various edge computation nodes is vulnerable to malicious attacks and eavesdropping,resulting in data and privacy leakage.We take secure offloading service into account and attempt to minimize the total cost of all offloading tasks by the optimal scheme,jointly considering offloading decision,security level and computing resource assignment.To reduce the total cost of all offloading tasks mainly caused by wireless communication,energy consumption and secure computing,we propose an optimization framework which can dynamically adjust the task offloading decision,security level and computing resource allocation policy simultaneously in MEC networks.We formalize the problem as a multi-agent decision based optimization problem,and further address it by using a multi-agent deep reinforcement learning(DRL)based method.Numerical results illustrate that the proposed DRL-based method is superior to the benchmark methods in terms of the total cost and support ratio. 展开更多
关键词 collaborative computation deep reinforcement learning mobile edge computing secure offloading task processing cost
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Secure Computation Efficiency Resource Allocation for Massive MIMO-Enabled Mobile Edge Computing Networks 认领 引用 被引量：2
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作者 Sun Gangcan Sun Jiwei +3 位作者 Hao Wanming Zhu Zhengyu Ji Xiang Zhou Yiqing 《China Communications》 SCIE CSCD 2024年第11期150-162,共13页
In this article,the secure computation efficiency(SCE)problem is studied in a massive multipleinput multiple-output(mMIMO)-assisted mobile edge computing(MEC)network.We first derive the secure transmission rate based ... In this article,the secure computation efficiency(SCE)problem is studied in a massive multipleinput multiple-output(mMIMO)-assisted mobile edge computing(MEC)network.We first derive the secure transmission rate based on the mMIMO under imperfect channel state information.Based on this,the SCE maximization problem is formulated by jointly optimizing the local computation frequency,the offloading time,the downloading time,the users and the base station transmit power.Due to its difficulty to directly solve the formulated problem,we first transform the fractional objective function into the subtractive form one via the dinkelbach method.Next,the original problem is transformed into a convex one by applying the successive convex approximation technique,and an iteration algorithm is proposed to obtain the solutions.Finally,the stimulations are conducted to show that the performance of the proposed schemes is superior to that of the other schemes. 展开更多
关键词 eavesdropping massive multiple input multiple output mobile edge computing partial offloading secure computation efficiency
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Online Optimization of Physical-Layer Secure Computation Offloading in Dynamic Environments 认领 引用 被引量：1
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作者 Chenshan Ren Wei Song +1 位作者 Lizhi Zhao Xiaobing Zhao 《China Communications》 SCIE CSCD 2020年第10期19-30,共12页
Mobile edge computing can provide powerful computation services around the end-users.However,given the broadcast nature of wireless transmissions,offloading the computation tasks via the uplink channels would raise se... Mobile edge computing can provide powerful computation services around the end-users.However,given the broadcast nature of wireless transmissions,offloading the computation tasks via the uplink channels would raise serious security concerns.This paper proposes an online approach to jointly optimize local processing,transmit power,and task offloading decisions without the a-priori knowledge of the dynamic environments.The proposed approach can guarantee the secure offloading and asymptotically minimize the time-average energy consumption of devices while maintaining the stability of the ergodic secrecy queues and task queues.By exploiting the Lyapunov optimization,the local processing,transmit power,and task offloading variables can be decoupled between time slots.The subproblems on local processing and computation offloading can be solved separately.Convex optimization and graph matching can be used to solve the computation offloading subproblem.Simulations show that the performances of the proposed approach are superior to other popular approaches. 展开更多
关键词 mobile edge computing physical-layer security Lyapunov optimization ergodic secrecy queue
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Privacy-preserving computation meets quantum computing:A scoping review 认领 引用
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作者 Aitor Gómez-Goiri Iñaki Seco-Aguirre +1 位作者 Oscar Lage Alejandra Ruiz 《Digital Communications and Networks》 SCIE EI CSCD 2025年第6期1707-1721,共15页
Privacy-Preserving Computation(PPC)comprises the techniques,schemes and protocols which ensure privacy and confidentiality in the context of secure computation and data analysis.Most of the current PPC techniques rely... Privacy-Preserving Computation(PPC)comprises the techniques,schemes and protocols which ensure privacy and confidentiality in the context of secure computation and data analysis.Most of the current PPC techniques rely on the complexity of cryptographic operations,which are expected to be efficiently solved by quantum computers soon.This review explores how PPC can be built on top of quantum computing itself to alleviate these future threats.We analyze quantum proposals for Secure Multi-party Computation,Oblivious Transfer and Homomorphic Encryption from the last decade focusing on their maturity and the challenges they currently face.Our findings show a strong focus on purely theoretical works,but a rise on the experimental consideration of these techniques in the last 5 years.The applicability of these techniques to actual use cases is an underexplored aspect which could lead to the practical assessment of these techniques. 展开更多
关键词 Quantum computing Privacy-preserving computation Oblivious transfer Secure multi-party computation Homomorphic encryption Scoping review
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Comparative Evaluation of Elliptic Curve Cryptography Based Homomorphic Encryption Schemes for a Novel Secure Multiparty Computation 认领 引用 被引量：1
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作者 Sankita J. Patel Ankit Chouhan Devesh C. Jinwala 《Journal of Information Security》 2014年第1期12-18,共7页
In this paper, we focus on Elliptic Curve Cryptography based approach for Secure Multiparty Computation (SMC) problem. Widespread proliferation of data and the growth of communication technologies have enabled collabo... In this paper, we focus on Elliptic Curve Cryptography based approach for Secure Multiparty Computation (SMC) problem. Widespread proliferation of data and the growth of communication technologies have enabled collaborative computations among parties in distributed scenario. Preserving privacy of data owned by parties is crucial in such scenarios. Classical approach to SMC is to perform computation using Trusted Third Party (TTP). However, in practical scenario, TTPs are hard to achieve and it is imperative to eliminate TTP in SMC. In addition, existing solutions proposed for SMC use classical homomorphic encryption schemes such as RSA and Paillier. Due to the higher cost incurred by such cryptosystems, the resultant SMC protocols are not scalable. We propose Elliptic Curve Cryptography (ECC) based approach for SMC that is scalable in terms of computational and communication cost and avoids TTP. In literature, there do exist various ECC based homomorphic schemes and it is imperative to investigate and analyze these schemes in order to select the suitable for a given application. In this paper, we empirically analyze various ECC based homomorphic encryption schemes based on performance metrics such as computational cost and communication cost. We recommend an efficient algorithm amongst several selected ones, that offers security with lesser overheads and can be applied in any application demanding privacy. 展开更多
关键词 Elliptic Curve Cryptography Privacy Preservation Secure Multiparty Computation
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