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
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
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
The increasing popularity of quantum computing has resulted in a considerable rise in demand for cloud quantum computing usage in recent years.Nevertheless,the rapid surge in demand for cloud-based quantum computing r...The increasing popularity of quantum computing has resulted in a considerable rise in demand for cloud quantum computing usage in recent years.Nevertheless,the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity.In order to meet the needs of an increasing number of researchers,it is imperative to facilitate efficient and flexible access to computing resources in a cloud environment.In this paper,we propose a novel quantum computing paradigm,Virtual QPU(VQPU),which addresses this issue and enhances quantum cloud throughput with guaranteed circuit fidelity.The proposal introduces three innovative concepts:(1)The integration of virtualization technology into the field of quantum computing to enhance quantum cloud throughput.(2)The introduction of an asynchronous execution of circuits methodology to improve quantum computing flexibility.(3)The development of a virtual QPU allocation scheme for quantum tasks in a cloud environment to improve circuit fidelity.The concepts have been validated through the utilization of a self-built simulated quantum cloud platform.展开更多
Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal sca...Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal scale tasks.In this study,we report an OECT-based neuromorphic device with tunable relaxation time(τ)by introducing an additional vertical back-gate electrode into a planar structure.The dual-gate design enablesτreconfiguration from 93 to 541 ms.The tunable relaxation behaviors can be attributed to the combined effects of planar-gate induced electrochemical doping and back-gateinduced electrostatic coupling,as verified by electrochemical impedance spectroscopy analysis.Furthermore,we used theτ-tunable OECT devices as physical reservoirs in the RC system for intelligent driving trajectory prediction,achieving a significant improvement in prediction accuracy from below 69%to 99%.The results demonstrate that theτ-tunable OECT shows a promising candidate for multi-temporal scale neuromorphic computing applications.展开更多
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
The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous c...The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods.展开更多
In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task schedul...In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.展开更多
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
As emerging two-dimensional(2D)materials,carbides and nitrides(MXenes)could be solid solutions or organized structures made up of multi-atomic layers.With remarkable and adjustable electrical,optical,mechanical,and el...As emerging two-dimensional(2D)materials,carbides and nitrides(MXenes)could be solid solutions or organized structures made up of multi-atomic layers.With remarkable and adjustable electrical,optical,mechanical,and electrochemical characteristics,MXenes have shown great potential in brain-inspired neuromorphic computing electronics,including neuromorphic gas sensors,pressure sensors and photodetectors.This paper provides a forward-looking review of the research progress regarding MXenes in the neuromorphic sensing domain and discussed the critical challenges that need to be resolved.Key bottlenecks such as insufficient long-term stability under environmental exposure,high costs,scalability limitations in large-scale production,and mechanical mismatch in wearable integration hinder their practical deployment.Furthermore,unresolved issues like interfacial compatibility in heterostructures and energy inefficiency in neu-romorphic signal conversion demand urgent attention.The review offers insights into future research directions enhance the fundamental understanding of MXene properties and promote further integration into neuromorphic computing applications through the convergence with various emerging technologies.展开更多
The advancement of flexible memristors has significantly promoted the development of wearable electronic for emerging neuromorphic computing applications.Inspired by in-memory computing architecture of human brain,fle...The advancement of flexible memristors has significantly promoted the development of wearable electronic for emerging neuromorphic computing applications.Inspired by in-memory computing architecture of human brain,flexible memristors exhibit great application potential in emulating artificial synapses for highefficiency and low power consumption neuromorphic computing.This paper provides comprehensive overview of flexible memristors from perspectives of development history,material system,device structure,mechanical deformation method,device performance analysis,stress simulation during deformation,and neuromorphic computing applications.The recent advances in flexible electronics are summarized,including single device,device array and integration.The challenges and future perspectives of flexible memristor for neuromorphic computing are discussed deeply,paving the way for constructing wearable smart electronics and applications in large-scale neuromorphic computing and high-order intelligent robotics.展开更多
High-entropy oxides(HEOs)have emerged as a promising class of memristive materials,characterized by entropy-stabilized crystal structures,multivalent cation coordination,and tunable defect landscapes.These intrinsic f...High-entropy oxides(HEOs)have emerged as a promising class of memristive materials,characterized by entropy-stabilized crystal structures,multivalent cation coordination,and tunable defect landscapes.These intrinsic features enable forming-free resistive switching,multilevel conductance modulation,and synaptic plasticity,making HEOs attractive for neuromorphic computing.This review outlines recent progress in HEO-based memristors across materials engineering,switching mechanisms,and synaptic emulation.Particular attention is given to vacancy migration,phase transitions,and valence-state dynamics—mechanisms that underlie the switching behaviors observed in both amorphous and crystalline systems.Their relevance to neuromorphic functions such as short-term plasticity and spike-timing-dependent learning is also examined.While encouraging results have been achieved at the device level,challenges remain in conductance precision,variability control,and scalable integration.Addressing these demands a concerted effort across materials design,interface optimization,and task-aware modeling.With such integration,HEO memristors offer a compelling pathway toward energy-efficient and adaptable brain-inspired electronics.展开更多
Neuromorphic devices have garnered significant attention as potential building blocks for energy-efficient hardware systems owing to their capacity to emulate the computational efficiency of the brain.In this regard,r...Neuromorphic devices have garnered significant attention as potential building blocks for energy-efficient hardware systems owing to their capacity to emulate the computational efficiency of the brain.In this regard,reservoir computing(RC)framework,which leverages straightforward training methods and efficient temporal signal processing,has emerged as a promising scheme.While various physical reservoir devices,including ferroelectric,optoelectronic,and memristor-based systems,have been demonstrated,many still face challenges related to compatibility with mainstream complementary metal oxide semiconductor(CMOS)integration processes.This study introduced a silicon-based schottky barrier metal-oxide-semiconductor field effect transistor(SB-MOSFET),which was fabricated under low thermal budget and compatible with back-end-of-line(BEOL).The device demonstrated short-term memory characteristics,facilitated by the modulation of schottky barriers and charge trapping.Utilizing these characteristics,a RC system for temporal data processing was constructed,and its performance was validated in a 5×4 digital classification task,achieving an accuracy exceeding 98%after 50 training epochs.Furthermore,the system successfully processed temporal signal in waveform classification and prediction tasks using time-division multiplexing.Overall,the SB-MOSFET's high compatibility with CMOS technology provides substantial advantages for large-scale integration,enabling the development of energy-efficient reservoir computing hardware.展开更多
Nowadays,advances in communication technology and cloud computing have spawned a variety of smart mobile devices,which will generate a great amount of computing-intensive businesses,and require corresponding resources...Nowadays,advances in communication technology and cloud computing have spawned a variety of smart mobile devices,which will generate a great amount of computing-intensive businesses,and require corresponding resources of computation and communication.Multiaccess edge computing(MEC)can offload computing-intensive tasks to the nearby edge servers,which alleviates the pressure of devices.Ultra-dense network(UDN)can provide effective spectrum resources by deploying a large number of micro base stations.Furthermore,network slicing can support various applications in different communication scenarios.Therefore,this paper integrates the ultra-dense network slicing and the MEC technology,and introduces a hybrid computing offloading strategy in order to satisfy various quality of service(QoS)of edge devices.In order to dynamically allocate limited resources,the above problem is formulated as multiagent distributed deep reinforcement learning(DRL),which will achieve low overhead computation offloading strategy and real-time resource allocation decisions.In this context,federated learning is added to train DRL agents in a distributed manner,where each agent is dedicated to exploring actions composed of offloading decisions and allocating resources,so as to jointly optimize system delay and energy consumption.Simulation results show that the proposed learning algorithm has better performance compared with other strategies in literature.展开更多
This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagno...This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency.To this end,a total of 3234 high-resolution images(2400×1080)were collected from three major rice diseases Rice Blast,Bacterial Blight,and Brown Spot—frequently found in actual rice cultivation fields.These images served as the training dataset.The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the backbone,thereby resulting in both model compression and improved inference speed.Additionally,YOLOv5-P,based on PP-PicoDet,was configured as a comparative model to quantitatively evaluate performance.Experimental results demonstrated that YOLOv5-V2 achieved excellent detection performance,with an mAP 0.5 of 89.6%,mAP 0.5–0.95 of 66.7%,precision of 91.3%,and recall of 85.6%,while maintaining a lightweight model size of 6.45 MB.In contrast,YOLOv5-P exhibited a smaller model size of 4.03 MB,but showed lower performance with an mAP 0.5 of 70.3%,mAP 0.5–0.95 of 35.2%,precision of 62.3%,and recall of 74.1%.This study lays a technical foundation for the implementation of smart agriculture and real-time disease diagnosis systems by proposing a model that satisfies both accuracy and lightweight requirements.展开更多
With the development of Internet of Things(IoT),the delay caused by network transmission has led to low data processing efficiency.At the same time,the limited computing power and available energy consumption of IoT t...With the development of Internet of Things(IoT),the delay caused by network transmission has led to low data processing efficiency.At the same time,the limited computing power and available energy consumption of IoT terminal devices are also the important bottlenecks that would restrict the application of blockchain,but edge computing could solve this problem.The emergence of edge computing can effectively reduce the delay of data transmission and improve data processing capacity.However,user data in edge computing is usually stored and processed in some honest-but-curious authorized entities,which leads to the leakage of users’privacy information.In order to solve these problems,this paper proposes a location data collection method that satisfies the local differential privacy to protect users’privacy.In this paper,a Voronoi diagram constructed by the Delaunay method is used to divide the road network space and determine the Voronoi grid region where the edge nodes are located.A random disturbance mechanism that satisfies the local differential privacy is utilized to disturb the original location data in each Voronoi grid.In addition,the effectiveness of the proposed privacy-preserving mechanism is verified through comparison experiments.Compared with the existing privacy-preserving methods,the proposed privacy-preserving mechanism can not only better meet users’privacy needs,but also have higher data availability.展开更多
Advanced cloud computing technology provides cost saving and flexibility of services for users.With the explosion of multimedia data,more and more data owners would outsource their personal multimedia data on the clou...Advanced cloud computing technology provides cost saving and flexibility of services for users.With the explosion of multimedia data,more and more data owners would outsource their personal multimedia data on the cloud.In the meantime,some computationally expensive tasks are also undertaken by cloud servers.However,the outsourced multimedia data and its applications may reveal the data owner’s private information because the data owners lose the control of their data.Recently,this thought has aroused new research interest on privacy-preserving reversible data hiding over outsourced multimedia data.In this paper,two reversible data hiding schemes are proposed for encrypted image data in cloud computing:reversible data hiding by homomorphic encryption and reversible data hiding in encrypted domain.The former is that additional bits are extracted after decryption and the latter is that extracted before decryption.Meanwhile,a combined scheme is also designed.This paper proposes the privacy-preserving outsourcing scheme of reversible data hiding over encrypted image data in cloud computing,which not only ensures multimedia data security without relying on the trustworthiness of cloud servers,but also guarantees that reversible data hiding can be operated over encrypted images at the different stages.Theoretical analysis confirms the correctness of the proposed encryption model and justifies the security of the proposed scheme.The computation cost of the proposed scheme is acceptable and adjusts to different security levels.展开更多
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
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
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
文摘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.
基金supported by the Basque Government through the ELKARTEK program for Research and Innovation,under the BRTAQUANTUM project(Grant Agreement No.KK-2022/00041)。
文摘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.
基金supported in part by the National Natural Science Foundation of China(62462053)the Science and Technology Foundation of Qinghai Province(2023-ZJ-731)+1 种基金the Open Project of the Qinghai Provincial Key Laboratory of Restoration Ecology in Cold Area(2023-KF-12)the Open Research Fund of Guangdong Key Laboratory of Blockchain Security,Guangzhou University。
文摘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.
文摘The increasing popularity of quantum computing has resulted in a considerable rise in demand for cloud quantum computing usage in recent years.Nevertheless,the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity.In order to meet the needs of an increasing number of researchers,it is imperative to facilitate efficient and flexible access to computing resources in a cloud environment.In this paper,we propose a novel quantum computing paradigm,Virtual QPU(VQPU),which addresses this issue and enhances quantum cloud throughput with guaranteed circuit fidelity.The proposal introduces three innovative concepts:(1)The integration of virtualization technology into the field of quantum computing to enhance quantum cloud throughput.(2)The introduction of an asynchronous execution of circuits methodology to improve quantum computing flexibility.(3)The development of a virtual QPU allocation scheme for quantum tasks in a cloud environment to improve circuit fidelity.The concepts have been validated through the utilization of a self-built simulated quantum cloud platform.
基金supported by the National Key Research and Development Program of China under Grant 2022YFB3608300in part by the National Nature Science Foundation of China(NSFC)under Grants 62404050,U2341218,62574056,62204052。
文摘Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal scale tasks.In this study,we report an OECT-based neuromorphic device with tunable relaxation time(τ)by introducing an additional vertical back-gate electrode into a planar structure.The dual-gate design enablesτreconfiguration from 93 to 541 ms.The tunable relaxation behaviors can be attributed to the combined effects of planar-gate induced electrochemical doping and back-gateinduced electrostatic coupling,as verified by electrochemical impedance spectroscopy analysis.Furthermore,we used theτ-tunable OECT devices as physical reservoirs in the RC system for intelligent driving trajectory prediction,achieving a significant improvement in prediction accuracy from below 69%to 99%.The results demonstrate that theτ-tunable OECT shows a promising candidate for multi-temporal scale neuromorphic computing applications.
基金supported by the Natural Science Foundation of China No.62362008the Major Scientific and Technological Special Project of Guizhou Province([2024]014).
文摘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.
基金appreciation to the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R384)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods.
基金supported and funded by theDeanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2503).
文摘In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.
文摘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.
基金supported by the NSFC(12474071)Natural Science Foundation of Shandong Province(ZR2024YQ051,ZR2025QB50)+6 种基金Guangdong Basic and Applied Basic Research Foundation(2025A1515011191)the Shanghai Sailing Program(23YF1402200,23YF1402400)funded by Basic Research Program of Jiangsu(BK20240424)Open Research Fund of State Key Laboratory of Crystal Materials(KF2406)Taishan Scholar Foundation of Shandong Province(tsqn202408006,tsqn202507058)Young Talent of Lifting engineering for Science and Technology in Shandong,China(SDAST2024QTB002)the Qilu Young Scholar Program of Shandong University。
文摘As emerging two-dimensional(2D)materials,carbides and nitrides(MXenes)could be solid solutions or organized structures made up of multi-atomic layers.With remarkable and adjustable electrical,optical,mechanical,and electrochemical characteristics,MXenes have shown great potential in brain-inspired neuromorphic computing electronics,including neuromorphic gas sensors,pressure sensors and photodetectors.This paper provides a forward-looking review of the research progress regarding MXenes in the neuromorphic sensing domain and discussed the critical challenges that need to be resolved.Key bottlenecks such as insufficient long-term stability under environmental exposure,high costs,scalability limitations in large-scale production,and mechanical mismatch in wearable integration hinder their practical deployment.Furthermore,unresolved issues like interfacial compatibility in heterostructures and energy inefficiency in neu-romorphic signal conversion demand urgent attention.The review offers insights into future research directions enhance the fundamental understanding of MXene properties and promote further integration into neuromorphic computing applications through the convergence with various emerging technologies.
基金supported by the NSFC(12474071)Natural Science Foundation of Shandong Province(ZR2024YQ051)+5 种基金Open Research Fund of State Key Laboratory of Materials for Integrated Circuits(SKLJC-K2024-12)the Shanghai Sailing Program(23YF1402200,23YF1402400)Natural Science Foundation of Jiangsu Province(BK20240424)Taishan Scholar Foundation of Shandong Province(tsqn202408006)Young Talent of Lifting engineering for Science and Technology in Shandong,China(SDAST2024QTB002)the Qilu Young Scholar Program of Shandong University.
文摘The advancement of flexible memristors has significantly promoted the development of wearable electronic for emerging neuromorphic computing applications.Inspired by in-memory computing architecture of human brain,flexible memristors exhibit great application potential in emulating artificial synapses for highefficiency and low power consumption neuromorphic computing.This paper provides comprehensive overview of flexible memristors from perspectives of development history,material system,device structure,mechanical deformation method,device performance analysis,stress simulation during deformation,and neuromorphic computing applications.The recent advances in flexible electronics are summarized,including single device,device array and integration.The challenges and future perspectives of flexible memristor for neuromorphic computing are discussed deeply,paving the way for constructing wearable smart electronics and applications in large-scale neuromorphic computing and high-order intelligent robotics.
基金financially supported by the National Natural Science Foundation of China(Grant No.12172093)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2021A1515012607)。
文摘High-entropy oxides(HEOs)have emerged as a promising class of memristive materials,characterized by entropy-stabilized crystal structures,multivalent cation coordination,and tunable defect landscapes.These intrinsic features enable forming-free resistive switching,multilevel conductance modulation,and synaptic plasticity,making HEOs attractive for neuromorphic computing.This review outlines recent progress in HEO-based memristors across materials engineering,switching mechanisms,and synaptic emulation.Particular attention is given to vacancy migration,phase transitions,and valence-state dynamics—mechanisms that underlie the switching behaviors observed in both amorphous and crystalline systems.Their relevance to neuromorphic functions such as short-term plasticity and spike-timing-dependent learning is also examined.While encouraging results have been achieved at the device level,challenges remain in conductance precision,variability control,and scalable integration.Addressing these demands a concerted effort across materials design,interface optimization,and task-aware modeling.With such integration,HEO memristors offer a compelling pathway toward energy-efficient and adaptable brain-inspired electronics.
基金supported in part by the Chinese Academy of Sciences(No.XDA0330302)NSFC program(No.22127901)。
文摘Neuromorphic devices have garnered significant attention as potential building blocks for energy-efficient hardware systems owing to their capacity to emulate the computational efficiency of the brain.In this regard,reservoir computing(RC)framework,which leverages straightforward training methods and efficient temporal signal processing,has emerged as a promising scheme.While various physical reservoir devices,including ferroelectric,optoelectronic,and memristor-based systems,have been demonstrated,many still face challenges related to compatibility with mainstream complementary metal oxide semiconductor(CMOS)integration processes.This study introduced a silicon-based schottky barrier metal-oxide-semiconductor field effect transistor(SB-MOSFET),which was fabricated under low thermal budget and compatible with back-end-of-line(BEOL).The device demonstrated short-term memory characteristics,facilitated by the modulation of schottky barriers and charge trapping.Utilizing these characteristics,a RC system for temporal data processing was constructed,and its performance was validated in a 5×4 digital classification task,achieving an accuracy exceeding 98%after 50 training epochs.Furthermore,the system successfully processed temporal signal in waveform classification and prediction tasks using time-division multiplexing.Overall,the SB-MOSFET's high compatibility with CMOS technology provides substantial advantages for large-scale integration,enabling the development of energy-efficient reservoir computing hardware.
文摘Nowadays,advances in communication technology and cloud computing have spawned a variety of smart mobile devices,which will generate a great amount of computing-intensive businesses,and require corresponding resources of computation and communication.Multiaccess edge computing(MEC)can offload computing-intensive tasks to the nearby edge servers,which alleviates the pressure of devices.Ultra-dense network(UDN)can provide effective spectrum resources by deploying a large number of micro base stations.Furthermore,network slicing can support various applications in different communication scenarios.Therefore,this paper integrates the ultra-dense network slicing and the MEC technology,and introduces a hybrid computing offloading strategy in order to satisfy various quality of service(QoS)of edge devices.In order to dynamically allocate limited resources,the above problem is formulated as multiagent distributed deep reinforcement learning(DRL),which will achieve low overhead computation offloading strategy and real-time resource allocation decisions.In this context,federated learning is added to train DRL agents in a distributed manner,where each agent is dedicated to exploring actions composed of offloading decisions and allocating resources,so as to jointly optimize system delay and energy consumption.Simulation results show that the proposed learning algorithm has better performance compared with other strategies in literature.
文摘This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency.To this end,a total of 3234 high-resolution images(2400×1080)were collected from three major rice diseases Rice Blast,Bacterial Blight,and Brown Spot—frequently found in actual rice cultivation fields.These images served as the training dataset.The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the backbone,thereby resulting in both model compression and improved inference speed.Additionally,YOLOv5-P,based on PP-PicoDet,was configured as a comparative model to quantitatively evaluate performance.Experimental results demonstrated that YOLOv5-V2 achieved excellent detection performance,with an mAP 0.5 of 89.6%,mAP 0.5–0.95 of 66.7%,precision of 91.3%,and recall of 85.6%,while maintaining a lightweight model size of 6.45 MB.In contrast,YOLOv5-P exhibited a smaller model size of 4.03 MB,but showed lower performance with an mAP 0.5 of 70.3%,mAP 0.5–0.95 of 35.2%,precision of 62.3%,and recall of 74.1%.This study lays a technical foundation for the implementation of smart agriculture and real-time disease diagnosis systems by proposing a model that satisfies both accuracy and lightweight requirements.
文摘With the development of Internet of Things(IoT),the delay caused by network transmission has led to low data processing efficiency.At the same time,the limited computing power and available energy consumption of IoT terminal devices are also the important bottlenecks that would restrict the application of blockchain,but edge computing could solve this problem.The emergence of edge computing can effectively reduce the delay of data transmission and improve data processing capacity.However,user data in edge computing is usually stored and processed in some honest-but-curious authorized entities,which leads to the leakage of users’privacy information.In order to solve these problems,this paper proposes a location data collection method that satisfies the local differential privacy to protect users’privacy.In this paper,a Voronoi diagram constructed by the Delaunay method is used to divide the road network space and determine the Voronoi grid region where the edge nodes are located.A random disturbance mechanism that satisfies the local differential privacy is utilized to disturb the original location data in each Voronoi grid.In addition,the effectiveness of the proposed privacy-preserving mechanism is verified through comparison experiments.Compared with the existing privacy-preserving methods,the proposed privacy-preserving mechanism can not only better meet users’privacy needs,but also have higher data availability.
基金This work was supported by the National Natural Science Foundation of China(No.61702276)the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology under Grant 2016r055 and the Priority Academic Program Development(PAPD)of Jiangsu Higher Education Institutions.The authors are grateful for the anonymous reviewers who made constructive comments and improvements.
文摘Advanced cloud computing technology provides cost saving and flexibility of services for users.With the explosion of multimedia data,more and more data owners would outsource their personal multimedia data on the cloud.In the meantime,some computationally expensive tasks are also undertaken by cloud servers.However,the outsourced multimedia data and its applications may reveal the data owner’s private information because the data owners lose the control of their data.Recently,this thought has aroused new research interest on privacy-preserving reversible data hiding over outsourced multimedia data.In this paper,two reversible data hiding schemes are proposed for encrypted image data in cloud computing:reversible data hiding by homomorphic encryption and reversible data hiding in encrypted domain.The former is that additional bits are extracted after decryption and the latter is that extracted before decryption.Meanwhile,a combined scheme is also designed.This paper proposes the privacy-preserving outsourcing scheme of reversible data hiding over encrypted image data in cloud computing,which not only ensures multimedia data security without relying on the trustworthiness of cloud servers,but also guarantees that reversible data hiding can be operated over encrypted images at the different stages.Theoretical analysis confirms the correctness of the proposed encryption model and justifies the security of the proposed scheme.The computation cost of the proposed scheme is acceptable and adjusts to different security levels.
基金supported by the National Natural Science Foundation of China(No.62206238)the Natural Science Foundation of Jiangsu Province(Grant No.BK20220562)the Natural Science Research Project of Universities in Jiangsu Province(No.22KJB520010).
文摘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.
基金supported in part by NSFC under Grant No.61172090National Science and Technology Major Project under Grant 2012ZX03002001+3 种基金Research Fund for the Doctoral Program of Higher Education of China under Grant No.20120201110013Scientific and Technological Project in Shaanxi Province under Grant(No.2012K06-30,No.2014JQ8322)Basic Science Research Fund in Xi'an Jiaotong University(No.XJJ2014049,No.XKJC2014008)Shaanxi Science and Technology Innovation Project(2013SZS16-Z01/P01/K01)
文摘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.
文摘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.
