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Privacy-preserving average consensus for multi-agent systems based on edge decomposition 认领 引用
1
作者 Zengwang JIN Qian LI +2 位作者 Biyun SHENG Changyin SUN Zhen WANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第5期451-465,共15页
This paper studies the problem of privacy preservation in achieving the average consensus of dynamic Multi-Agent Systems(MAS).Average consensus performs an essential role in dynamic MAS to promote collaboration,coordi... This paper studies the problem of privacy preservation in achieving the average consensus of dynamic Multi-Agent Systems(MAS).Average consensus performs an essential role in dynamic MAS to promote collaboration,coordinate decision-making,resolve conflicts,and enhance system reliability.The process of achieving average consensus requires the information exchange between agents,which raises concerns about sensitive data leakage.To address this issue,we propose a novel algorithm that combines state decomposition with edge characteristics in network topology to protect the critical data during the average consensus process.Specifically,the original state of each agent is decomposed into|Ni|+1 substates,where|Ni|represents the number of neighboring nodes.For each agent,the public substate performs the function of the original state to participate in computation and interaction between other agents,while the private parts only interact with the first one of the same agent and keep invisible to other agents.Unlike other approaches that focus solely on the privacy preservation of agents'initial state information,this paper extends to dynamic state of agents at every moment.Next,rigorous proofs of the accuracy in average consensus are provided.Furthermore,it is shown that privacy can be protected by employing our algorithm if agent i has at least one neighbor who is not an honest-but-curious agent.As for external eavesdroppers,a sufficient condition is presented that the state information is not estimated with any guaranteed accuracy.Finally,numerical simulations are presented to verify the effectiveness of our approach. 展开更多
关键词 Multi-agent systems Privacy-preserving mechanism Average consensus Network topology Edge decomposition
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Secformer:Privacy-preserving atomic-level componentized transformer-like model with MPC 认领 引用
2
作者 Chi Zhang Tao Shen +3 位作者 Fenhua Bai Kai Zeng Xiaohui Zhang Bin Cao 《Digital Communications and Networks》 SCIE EI CSCD 2026年第1期86-100,共15页
The global surge in Artificial Intelligence(AI)has been triggered by the impressive performance of deep-learning models based on the Transformer architecture.However,the efficacy of such models is increasingly depende... The global surge in Artificial Intelligence(AI)has been triggered by the impressive performance of deep-learning models based on the Transformer architecture.However,the efficacy of such models is increasingly dependent on the volume and quality of data.Data are often distributed across institutions and companies,making cross-organizational data transfer vulnerable to privacy breaches and subject to privacy laws and trade secret regulations.These privacy and security concerns continue to pose major challenges to collaborative training and inference in multi-source data environments.These challenges are particularly significant for Transformer models,where the complex internal encryption computations drastically reduce computational efficiency,ultimately threatening the model's practical applicability.We hence introduce Secformer,an innovative architecture specifically designed to protect the privacy of Transformer-like models.Secformer separates the encoder and decoder modules,enabling the decomposition of computation flows in Transformer-like models and their efficient mapping to Multi-Party Computation(MPC)protocols.This design effectively addresses privacy leakage issues during the collaborative computation process of Transformer models.To prevent performance degradation caused by encrypted attention modules,we propose a modular design strategy that optimizes high-level components by reconstructing low-level operators.We further analyze the security of Secformer's core components,presenting security definitions and formal proofs.We construct a library of fundamental operators and core modules using atomic-level component designs as the basic building blocks for encoders and decoders.Moreover,these components can serve as foundational operators for other Transformer-like models.Extensive experimental evaluations demonstrate Secformer's excellent performance while preserving privacy and offering universal adaptability for Transformer-like models. 展开更多
关键词 Privacy-preserving computation Deep learning Multi-party computation Data sharing
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Hybrid Ensemble and Federated Learning Framework for Privacy-Preserving Cardiovascular MRI Segmentation 认领 引用
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作者 Karim Gasmi Afrah Alanazi +4 位作者 Inam Alanazi Sahar Almenwer Norah Alanazi Sarah Almaghrabi Samia Yahyaoui 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期1245-1287,共43页
Cardiac magnetic resonance imaging(MRI)segmentation is an essential aspect of quantitative cardiovascular analysis,facilitating accurate evaluation of ventricular volumes,myocardial mass,and functional parameters.Deep... Cardiac magnetic resonance imaging(MRI)segmentation is an essential aspect of quantitative cardiovascular analysis,facilitating accurate evaluation of ventricular volumes,myocardial mass,and functional parameters.Deep learning-based segmentation models have shown strong performance on benchmark datasets such as ACDC,but they remain challenging to deploy in real-world multi-centre settings.Data privacy laws make it hard to share data across institutions,and differences in imaging protocols and patient populationsmean that data is not always distributed in the same way(non-IID).This can have a big impact on how wellmodels work together and how well they generalise.To address these issues,we first evaluate advanced segmentation architectures,including UNet++and FPN with EfficientNet-based encoders,and assess multiple hybrid combinations at the probability level.We further improve the ensemble strategy by using a genetic algorithm to automatically identify the optimal model-weighting scheme,rather than fixed combination coefficients.The genetic algorithm explores the solution space to identify the optimal weight configuration based on segmentation metrics.The best hybrid configuration is then chosen as the input architecture for the federated learning stage.We propose a privacy-preserving federated ensemble framework that enables multiple clients to collaboratively train segmentation models without sharing raw MRI data.We methodically evaluate three federated optimisation strategies:FedAvg under IID and non-IID client distributions,and FedProx,which incorporates proximal regularisation to reduce client drift.The genetically optimised ensemble is always used in all federated setups.A thorough analysis of ACDC testing volumes employing overlap-and boundary-based metrics illustrates that the amalgamation of hybrid learning with genetic optimisation and federated training enhances robustness in heterogeneous environments while maintaining data confidentiality,thus providing an efficient approach for secure multi-centre cardiac MRI segmentation. 展开更多
关键词 SDG 3 cardiovascular imaging segmentation ensemble deep learning genetic algorithm federated learning privacy-preserving AI
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Privacy-Preserving Gender-Based Customer Behavior Analytics in Retail Spaces Using Computer Vision 认领 引用
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作者 Ginanjar Suwasono Adi Samsul Huda +4 位作者 Griffani Megiyanto Rahmatullah Dodit Suprianto Dinda Qurrota Aini Al-Sefy Ivon Sandya Sari Putri Lalu Tri Wijaya Nata Kusuma 《Computers, Materials & Continua》 SCIE EI 2026年第1期1839-1861,共23页
In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and ta... In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and targeted marketing.However,existing computer vision solutions often rely on facial recognition to gather such insights,raising significant privacy and ethical concerns.To address these issues,this paper presents a privacypreserving customer analytics system through two key strategies.First,we deploy a deep learning framework using YOLOv9s,trained on the RCA-TVGender dataset.Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification.Second,we apply AES-128 encryption to customer position data,ensuring secure access and regulatory compliance.Our system achieved overall performance,with 81.5%mAP@50,77.7%precision,and 75.7%recall.Moreover,a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers.For instance,women spent more time in certain areas and showed interest in different products.These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy. 展开更多
关键词 Business intelligence customer behavior privacy-preserving analytics computer vision deep learning smart retail gender recognition heatmap privacy RCA-TVGender dataset
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Secure and Differentially Private Edge-Cloud Federated Learning Framework for Privacy-Preserving Maritime AIS Intelligence 认领 引用
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作者 Abuzar Khan Abid Iqbal +3 位作者 Ghassan Husnain Fahad Masood Mohammed Al-Naeem Sajid Iqbal 《Computers, Materials & Continua》 SCIE EI 2026年第6期658-673,共16页
Cloud computing now supports large-scale maritime analytics,yet offloading rich Automatic Identification System(AIS)data to the cloud exposes sensitive operational patterns and complicates compliance with cross-border... Cloud computing now supports large-scale maritime analytics,yet offloading rich Automatic Identification System(AIS)data to the cloud exposes sensitive operational patterns and complicates compliance with cross-border privacy regulations.This work addresses the gap between growing demand for AI-driven vessel intelligence and the limited availability of practical,privacy-preserving cloud solutions.We introduce a privacy-by-design edge-cloud framework in which ports and vessels serve as federated clients,training vessel-type classifiers on local AIS trajectories while transmitting only clipped,Gaussian-perturbed updates to a zero-trust cloud coordinator employing secure and robust aggregation.Using a public AIS corpus with realistic non-IID client partitions,our evaluation shows that non-private FedAvg attains validation AUC≈0.90 and test AUC≈0.78,closely matching a centralized baseline.Moderate differential privacy noise(δ≤0.5)preserves most of this utility across KRUM and trimmed-mean aggregation.Communication analysis indicates that secure aggregation introduces negligible overhead compared with standard FedAvg,while homomorphic encryption increases payload size by roughly an order of magnitude.Membership-inference experiments further demonstrate strong privacy protection,yielding ROC AUC≈0.51 with no correctly inferred training members.Overall,the findings show that effective,regulation-conscious maritime analytics can be achieved without centralizing raw AIS data,offering a practical pathway for deploying resilient,privacy-enhanced AI services in distributed maritime environments. 展开更多
关键词 Federated learning privacy-preserving cloud computing maritime AIS analytics differential privacy
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A Privacy-Preserving Convolutional Neural Network Inference Framework for AIoT Applications 认领 引用
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作者 Haoran Wang Shuhong Yang +2 位作者 Kuan Shao Tao Xiao Zhenyong Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第1期1354-1371,共18页
With the rapid development of the Artificial Intelligence of Things(AIoT),convolutional neural networks(CNNs)have demonstrated potential and remarkable performance in AIoT applications due to their excellent performan... With the rapid development of the Artificial Intelligence of Things(AIoT),convolutional neural networks(CNNs)have demonstrated potential and remarkable performance in AIoT applications due to their excellent performance in various inference tasks.However,the users have concerns about privacy leakage for the use of AI and the performance and efficiency of computing on resource-constrained IoT edge devices.Therefore,this paper proposes an efficient privacy-preserving CNN framework(i.e.,EPPA)based on the Fully Homomorphic Encryption(FHE)scheme for AIoT application scenarios.In the plaintext domain,we verify schemes with different activation structures to determine the actual activation functions applicable to the corresponding ciphertext domain.Within the encryption domain,we integrate batch normalization(BN)into the convolutional layers to simplify the computation process.For nonlinear activation functions,we use composite polynomials for approximate calculation.Regarding the noise accumulation caused by homomorphic multiplication operations,we realize the refreshment of ciphertext noise through minimal“decryption-encryption”interactions,instead of adopting bootstrapping operations.Additionally,in practical implementation,we convert three-dimensional convolution into two-dimensional convolution to reduce the amount of computation in the encryption domain.Finally,we conduct extensive experiments on four IoT datasets,different CNN architectures,and two platforms with different resource configurations to evaluate the performance of EPPA in detail. 展开更多
关键词 Artificial Intelligence of Things(AIoT) convolutional neural network privacy-preserving fully homomorphic encryption
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Privacy-Preserving Federated Learning for EEG-Based Biometric Recognition in AI-Enabled Epilepsy Detection 认领 引用
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作者 Qiuhao Xu Chen Wang +3 位作者 Xi Wen Lurong Jiang Wenying Zheng Zhengkui Chen 《Computers, Materials & Continua》 SCIE EI 2026年第9期882-896,共15页
The convergence of Generative Artificial Intelligence and biometric recognition is reshaping modern healthcare.It enables more adaptive and intelligent human–machine interactions.Epilepsy,a common neurological disord... The convergence of Generative Artificial Intelligence and biometric recognition is reshaping modern healthcare.It enables more adaptive and intelligent human–machine interactions.Epilepsy,a common neurological disorder affecting millions worldwide,relies heavily on electroencephalography(EEG)signals for diagnosis and monitoring.Wearable consumer devices with EEG sensors support continuous physiological data collection.However,transmitting sensitive biometric data to centralized servers introduces serious privacy and security risks.Federated learning(FL)provides a distributed training framework that keeps raw data on local devices.Despite this advantage,existing FL methods remain vulnerable to gradient leakage attacks,where adversaries may infer private biometric information from shared model updates.To address this issue,we propose PFED,a privacy-preserving federated learning protocol that combines randomized group interaction with volunteer-assisted secure aggregation.The proposed method effectively obfuscates gradient information while maintaining model utility.Experiments on the public CHB-MIT EEG dataset show that PFED achieves reliable epilepsy detection performance and strong privacy protection.These results demonstrate its potential for secure AI-enabled biometric healthcare applications in consumer device environments. 展开更多
关键词 Federated learning secure aggregation privacy-preserving epilepsy detection biometric recognition
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Federated Learning for Malicious Domain Detection via Privacy-Preserving DNS Traffic Analysis 认领 引用
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作者 Samar Abbas Mangi Samina Rajper +1 位作者 Noor Ahmed Shaikh Shehzad Ashraf Chaudhry 《Computers, Materials & Continua》 SCIE EI 2026年第6期2131-2150,共20页
Malicious domain detection(MDD)from DNS telemetry enables early threat hunting but is constrained by privacy and data-sharing barriers across organizations.We present a deployable federated learning(FL)pipeline that t... Malicious domain detection(MDD)from DNS telemetry enables early threat hunting but is constrained by privacy and data-sharing barriers across organizations.We present a deployable federated learning(FL)pipeline that trains a compact deep neural network(DNN;64-32-16 with ReLU and dropout 0.3)locally at each client and exchanges only masked model updates.Privacy is enforced via secure aggregation(the server observes only an aggregate of masked updates)and optional server-side differential privacy(DP)via clipping and Gaussian noise.Our feature schema combines DNS-specific lexical cues(character n-grams,entropy,TLD indicators)with lightweight behavioral signals(TTL dispersion,query cadence)without exporting raw logs or identifiers.We benchmark FedAvg,FedProx,and FedNova under controlled non-IID client partitions and report ROC-AUC,precision-recall area under the curve(PR-AUC),F1,convergence speed,and communication cost.Federated models approach centralized training while outperforming local-only baselines;FedProx reaches the target Accuracy≥0.995 in fewer rounds than FedAvg under medium heterogeneity.We report 95%bootstrap confidence intervals and paired significance tests(DeLong for ROC-AUC;McNemar for Accuracy).Overall,privacy-preserving FL for DNS-based MDD is practical,providing near-centralized utility while keeping DNS data local. 展开更多
关键词 Federated learning DNS security malicious domain detection privacy-preserving analytics secure aggregation differential privacy
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Privacy-Preserving Phishing Detection:A Systematic Review of LLMs,Federated Learning,and Blockchain Integration 认领 引用
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作者 Ghadi Almaktoom Suliman Aladhadh Salim El Khediri 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期236-269,共34页
The rapid growth of phishing attempts in the enterprise could potentially lead to bankruptcy.The primary focus of the research is on detecting phishing attacks,with no interest in how the data is processed.Attackers u... The rapid growth of phishing attempts in the enterprise could potentially lead to bankruptcy.The primary focus of the research is on detecting phishing attacks,with no interest in how the data is processed.Attackers use fraudulent methods to obtain valuable,confidential information,resulting in billions of dollars in financial losses for enterprises.In our review,we examined the methods used in phishing-detection studies.We concluded that the two main sections,centralized and decentralized methods,were the centralized ones,which aggregate data in a central server and thus violate data protection regulations,such as GDPR.In order to properly investigate the field,we put four main questions to give the reader a proper understanding of the field:what are the major detection approaches,what are their limitations and gaps,which datasets are most commonly used and trusted across different studies,and which privacy-preserving detection approaches are used and investigated in the field of phishing detection.To address these questions,we examined 105 different papers published from 2015 to 2024.Our review covers machine learning,deep learning,hybrid methods,large language models(LLMs),federated learning,and blockchain-based detection.Our investigation led to centralized approaches that achieved more than 95%accuracy but raised privacy concerns.Keeping data local on user devices offers privacy protection,as in decentralized strategies such as federated learning,at the cost of an accuracy trade-off of 1%3%.Other decentralized methods,such as blockchain-based systems,enhance security and transparency in the pricing of computational challenges. 展开更多
关键词 Phishing detection federated learning blockchain privacy-preserving deep learning BERT natural language processing smart contracts decentralized machine learning cybersecurity
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Privacy-Preserving Federated Malware Detection Using Memory and Behavioral Features 认领 引用
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作者 Ammar Odeh Osama Alhaj Hassan Anas Abu Taleb 《Computers, Materials & Continua》 SCIE EI 2026年第8期927-947,共21页
The rapid growth of sophisticated malware and the increasing diversity of computing environments have exposed critical limitations in traditional centralized malware detection systems,particularly in data privacy,scal... The rapid growth of sophisticated malware and the increasing diversity of computing environments have exposed critical limitations in traditional centralized malware detection systems,particularly in data privacy,scalability,and adaptability.This study proposes a privacy-preserving,collaborative malware-detection framework that leverages federated learning to improve detection accuracy while keeping sensitive data local to participating devices.The objective is to address emerging malware threats by combining behavioral and memory-based analysis within a decentralized learning paradigm.The proposed framework employs federated learning to train a global malware detection model without transferring raw data.Each client locally extracts discriminative features derived from system behavior and memory artifacts,including process activity patterns,memory access characteristics,and runtime indicators.Local deep learning models are trained independently,and only model parameters are shared with a central aggregator,which constructs an optimized global model through iterative parameter aggregation.This approach significantly reduces privacy risks and communication overhead compared to centralized training.Experimental evaluations on benchmark malware datasets demonstrate that the proposed federated approach achieves detection performance comparable to,and in some cases exceeding,that of centralized deep learning models.The results indicate improved robustness against previously unseen malware variants,with high detection accuracy and reduced false positive rates.Furthermore,privacy is preserved throughout the learning process,making the framework suitable for real-world distributed,resource-constrained environments.The findings confirm that federated learning,combined with memory and behavioral feature analysis,provides an effective,privacy-aware solution for modern malware detection.This work contributes to recent advances in cybersecurity by offering a scalable,secure,and practical detection framework that can be deployed across distributed systems,including enterprise networks and edge computing environments. 展开更多
关键词 Malware detection federated learning privacy-preserving machine learning memory forensics behavioral analysis deep learning distributed cybersecurity threat intelligence
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In-Mig:Geographically Dispersed Agentic LLMs for Privacy-Preserving Artificial Intelligence 认领 引用
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作者 Mohammad Nauman 《Computers, Materials & Continua》 SCIE EI 2026年第5期1101-1115,共15页
Large LanguageModels(LLMs)are increasingly utilized for semantic understanding and reasoning,yet their use in sensitive settings is limited by privacy concerns.This paper presents In-Mig,a mobile-agent architecture th... Large LanguageModels(LLMs)are increasingly utilized for semantic understanding and reasoning,yet their use in sensitive settings is limited by privacy concerns.This paper presents In-Mig,a mobile-agent architecture that integrates LLM reasoning within agents that can migrate across organizational venues.Unlike centralized approaches,In-Mig performs reasoning in situ,ensuring that raw data remains within institutional boundaries while allowing for cross-venue synthesis.The architecture features a policy-scoped memory model,utility-driven route planning,and cryptographic trust enforcement.Aprototype using JADE for mobility and quantizedMistral-7B demonstrates practical feasibility.Evaluation across various scenarios shows that In-Mig achieves 92%similarity to centralized baselines,confirming its utility and strong privacy guarantees.These results suggest that migrating,privacy-preserving LLM agents can effectively support decentralized reasoning in trust-sensitive domains. 展开更多
关键词 Mobile agents large language models(LLMs) privacy-preserving AI decentralized reasoning trust and security
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Privacy-Preserving Collaborative Task Allocation for Multi-Skill Mobile Crowdsensing 认领 引用
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作者 Jie Li Fuyuan Song Qin Jiang 《Computers, Materials & Continua》 SCIE EI 2026年第10期2408-2432,共25页
Mobile crowdsensing enables large-scale sensing tasks through smart devices carried by users and has been widely applied in intelligent transportation and environmental monitoring.With the increasing complexity of sen... Mobile crowdsensing enables large-scale sensing tasks through smart devices carried by users and has been widely applied in intelligent transportation and environmental monitoring.With the increasing complexity of sensing tasks,many tasks require the collaboration of multiple workers with different skills.However,both task-required skills and worker skills are privacy-sensitive,and directly exposing them to the platform may reveal task intentions and workers’capability profiles.To address this issue,this paper proposes Dual-Fog Privacy-Preserving Multi-skill Task Allocation(DPMTA),a privacy-preserving task allocation scheme for multi-skill collaborative tasks.DPMTA adopts a dual-fog architecture to separately protect location privacy and skill privacy.Specifically,task and worker locations are perturbed by differential privacy,while skill information is split by XOR secret sharing and distributed to two non-colluding fog servers.With Beaver triple-assisted secure Boolean computation,DPMTA enables skill matching,coverage updating,and collaborative worker selection without revealing plaintext skills.In addition,a random permutation mechanism is introduced to hide the direct mapping between skill bit positions and semantic skill labels.Security analysis shows that DPMTA effectively protects skill and location privacy.Meanwhile,the experimental evaluation demonstrates that DPMTA maintains a high task allocation success rate while keeping communication and computation overhead within acceptable limits. 展开更多
关键词 Privacy-preserving task allocation secret sharing mobile crowdsensing(MCS)
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VPAFL: Verifiable Privacy-Preserving Aggregation for Federated Learning Based on Single Server 认领 引用 被引量：1
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作者 Peizheng Lai Minqing Zhang +2 位作者 Yixin Tang Ya Yue Fuqiang Di 《Computers, Materials & Continua》 SCIE EI 2025年第8期2935-2957,共23页
Federated Learning(FL)has emerged as a promising distributed machine learning paradigm that enables multi-party collaborative training while eliminating the need for raw data sharing.However,its reliance on a server i... Federated Learning(FL)has emerged as a promising distributed machine learning paradigm that enables multi-party collaborative training while eliminating the need for raw data sharing.However,its reliance on a server introduces critical security vulnerabilities:malicious servers can infer private information from received local model updates or deliberately manipulate aggregation results.Consequently,achieving verifiable aggregation without compromising client privacy remains a critical challenge.To address these problem,we propose a reversible data hiding in encrypted domains(RDHED)scheme,which designs joint secret message embedding and extraction mechanism.This approach enables clients to embed secret messages into ciphertext redundancy spaces generated during model encryption.During the server aggregation process,the embedded messages from all clients fuse within the ciphertext space to form a joint embedding message.Subsequently,clients can decrypt the aggregated results and extract this joint embedding message for verification purposes.Building upon this foundation,we integrate the proposed RDHED scheme with linear homomorphic hash and digital signatures to design a verifiable privacy-preserving aggregation protocol for single-server architectures(VPAFL).Theoretical proofs and experimental analyses show that VPAFL can effectively protect user privacy,achieve lightweight computational and communication overhead of users for verification,and present significant advantages with increasing model dimension. 展开更多
关键词 Verifiable federated learning privacy-preserving homomorphic encryption reversible data hiding in encrypted domain secret sharing
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Securing Internet of Things Devices with Federated Learning:A Privacy-Preserving Approach for Distributed Intrusion Detection 认领 引用 被引量：1
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作者 Sulaiman Al Amro 《Computers, Materials & Continua》 SCIE EI 2025年第6期4623-4658,共36页
The rapid proliferation of Internet of Things(IoT)devices has heightened security concerns,making intrusion detection a pivotal challenge in safeguarding these networks.Traditional centralized Intrusion Detection Syst... The rapid proliferation of Internet of Things(IoT)devices has heightened security concerns,making intrusion detection a pivotal challenge in safeguarding these networks.Traditional centralized Intrusion Detection Systems(IDS)often fail to meet the privacy requirements and scalability demands of large-scale IoT ecosystems.To address these challenges,we propose an innovative privacy-preserving approach leveraging Federated Learning(FL)for distributed intrusion detection.Our model eliminates the need for aggregating sensitive data on a central server by training locally on IoT devices and sharing only encrypted model updates,ensuring enhanced privacy and scalability without compromising detection accuracy.Key innovations of this research include the integration of advanced deep learning techniques for real-time threat detection with minimal latency and a novel model to fortify the system’s resilience against diverse cyber-attacks such as Distributed Denial of Service(DDoS)and malware injections.Our evaluation on three benchmark IoT datasets demonstrates significant improvements:achieving 92.78%accuracy on NSL-KDD,91.47%on BoT-IoT,and 92.05%on UNSW-NB15.The precision,recall,and F1-scores for all datasets consistently exceed 91%.Furthermore,the communication overhead was reduced to 85 MB for NSL-KDD,105 MB for BoT-IoT,and 95 MB for UNSW-NB15—substantially lower than traditional centralized IDS approaches.This study contributes to the domain by presenting a scalable,secure,and privacy-preserving solution tailored to the unique characteristics of IoT environments.The proposed framework is adaptable to dynamic and heterogeneous settings,with potential applications extending to other privacy-sensitive domains.Future work will focus on enhancing the system’s efficiency and addressing emerging challenges such as model poisoning attacks in federated environments. 展开更多
关键词 Federated learning internet of things intrusion detection privacy-preserving distributed security
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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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EPRFL:An Efficient Privacy-Preserving and Robust Federated Learning Scheme for Fog Computing 认领 引用
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作者 Ke Zhijie Xie Yong +1 位作者 Syed Hamad Shirazi Li Haifeng 《China Communications》 SCIE EI CSCD 2025年第4期202-222,共21页
Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple parties.When combined with Fog Computing,FL offers enhanced capabilities for machin... Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple parties.When combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data aggregation.To address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing scenarios.Specifically,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node dropping.Then,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication overhead.To minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive score.Theoretical analysis demonstrates that EPRFL offers robust security and low latency.Extensive experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency. 展开更多
关键词 federated learning fog computing internet of things privacy-preserving robustness
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Secure and Privacy-Preserving Cross-Departmental Computation Framework Based on BFV and Blockchain 认领 引用
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作者 Peng Zhao Yu Du 《Journal of Electronic Research and Application》 2025年第6期207-217,共11页
As the demand for cross-departmental data collaboration continues to grow,traditional encryption methods struggle to balance data privacy with computational efficiency.This paper proposes a cross-departmental privacy-... As the demand for cross-departmental data collaboration continues to grow,traditional encryption methods struggle to balance data privacy with computational efficiency.This paper proposes a cross-departmental privacy-preserving computation framework based on BFV homomorphic encryption,threshold decryption,and blockchain technology.The proposed scheme leverages homomorphic encryption to enable secure computations between sales,finance,and taxation departments,ensuring that sensitive data remains encrypted throughout the entire process.A threshold decryption mechanism is employed to prevent single-point data leakage,while blockchain and IPFS are integrated to ensure verifiability and tamper-proof storage of computation results.Experimental results demonstrate that with 5,000 sample data entries,the framework performs efficiently and is highly scalable in key stages such as sales encryption,cost calculation,and tax assessment,thereby validating its practical feasibility and security. 展开更多
关键词 Homomorphic encryption Zero-knowledge proof Blockchain Cross-departmental privacy-preserving computation
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VPFL:A verifiable privacy-preserving federated learning scheme for edge computing systems 认领 引用 被引量：7
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作者 Jiale Zhang Yue Liu +3 位作者 Di Wu Shuai Lou Bing Chen Shui Yu 《Digital Communications and Networks》 SCIE CSCD 2023年第4期981-989,共9页
Federated learning for edge computing is a promising solution in the data booming era,which leverages the computation ability of each edge device to train local models and only shares the model gradients to the centra... Federated learning for edge computing is a promising solution in the data booming era,which leverages the computation ability of each edge device to train local models and only shares the model gradients to the central server.However,the frequently transmitted local gradients could also leak the participants’private data.To protect the privacy of local training data,lots of cryptographic-based Privacy-Preserving Federated Learning(PPFL)schemes have been proposed.However,due to the constrained resource nature of mobile devices and complex cryptographic operations,traditional PPFL schemes fail to provide efficient data confidentiality and lightweight integrity verification simultaneously.To tackle this problem,we propose a Verifiable Privacypreserving Federated Learning scheme(VPFL)for edge computing systems to prevent local gradients from leaking over the transmission stage.Firstly,we combine the Distributed Selective Stochastic Gradient Descent(DSSGD)method with Paillier homomorphic cryptosystem to achieve the distributed encryption functionality,so as to reduce the computation cost of the complex cryptosystem.Secondly,we further present an online/offline signature method to realize the lightweight gradients integrity verification,where the offline part can be securely outsourced to the edge server.Comprehensive security analysis demonstrates the proposed VPFL can achieve data confidentiality,authentication,and integrity.At last,we evaluate both communication overhead and computation cost of the proposed VPFL scheme,the experimental results have shown VPFL has low computation costs and communication overheads while maintaining high training accuracy. 展开更多
关键词 Federated learning Edge computing Privacy-preserving Verifiable aggregation Homomorphic cryptosystem
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A Retrievable Data Perturbation Method Used in Privacy-Preserving in Cloud Computing 认领 引用 被引量：6
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作者 YANG Pan GUI Xiaolin +3 位作者 AN Jian YAO Jing LIN Jiancai TIAN Feng 《China Communications》 SCIE CSCD 2014年第8期73-84,共12页
With the increasing popularity of cloud computing,privacy has become one of the key problem in cloud security.When data is outsourced to the cloud,for data owners,they need to ensure the security of their privacy;for ... With the increasing popularity of cloud computing,privacy has become one of the key problem in cloud security.When data is outsourced to the cloud,for data owners,they need to ensure the security of their privacy;for cloud service providers,they need some information of the data to provide high QoS services;and for authorized users,they need to access to the true value of data.The existing privacy-preserving methods can't meet all the needs of the three parties at the same time.To address this issue,we propose a retrievable data perturbation method and use it in the privacy-preserving in data outsourcing in cloud computing.Our scheme comes in four steps.Firstly,an improved random generator is proposed to generate an accurate"noise".Next,a perturbation algorithm is introduced to add noise to the original data.By doing this,the privacy information is hidden,but the mean and covariance of data which the service providers may need remain unchanged.Then,a retrieval algorithm is proposed to get the original data back from the perturbed data.Finally,we combine the retrievable perturbation with the access control process to ensure only the authorized users can retrieve the original data.The experiments show that our scheme perturbs date correctly,efficiently,and securely. 展开更多
关键词 privacy-preserving data perturbation retrieval access control cloudcomputing
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A Survey on the Privacy-Preserving Data Aggregation in Wireless Sensor Networks 认领 引用 被引量：5
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作者 XU Jian YANG Geng +1 位作者 CHEN Zhengyu WANG Qianqian 《China Communications》 SCIE CSCD 2015年第5期162-180,共19页
Wireless sensor networks(WSNs)consist of a great deal of sensor nodes with limited power,computation,storage,sensing and communication capabilities.Data aggregation is a very important technique,which is designed to s... Wireless sensor networks(WSNs)consist of a great deal of sensor nodes with limited power,computation,storage,sensing and communication capabilities.Data aggregation is a very important technique,which is designed to substantially reduce the communication overhead and energy expenditure of sensor node during the process of data collection in a WSNs.However,privacy-preservation is more challenging especially in data aggregation,where the aggregators need to perform some aggregation operations on sensing data it received.We present a state-of-the art survey of privacy-preserving data aggregation in WSNs.At first,we classify the existing privacy-preserving data aggregation schemes into different categories by the core privacy-preserving techniques used in each scheme.And then compare and contrast different algorithms on the basis of performance measures such as the privacy protection ability,communication consumption,power consumption and data accuracy etc.Furthermore,based on the existing work,we also discuss a number of open issues which may intrigue the interest of researchers for future work. 展开更多
关键词 wireless sensor networks data aggregation privacy-preserving
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