The issue of privacy leakage in distributed consensus has garnered significant attention over the years,but existing studies often overlook the challenges posed by limited communication in algorithm design.This paper ...The issue of privacy leakage in distributed consensus has garnered significant attention over the years,but existing studies often overlook the challenges posed by limited communication in algorithm design.This paper addresses the issue of privacy preservation in distributed weighted average consensus under limited communication scenarios.Specifically targeting directed and unbalanced topologies,we propose a privacy-preserving implementation protocol that incorporates the Paillier homomorphic encryption scheme.The protocol encrypts only the 1-bit quantized messages exchanged between agents,thus ensuring both the correctness of the consensus result and the confidentiality of each agent's initial state.To demonstrate the practicality of the proposed method,we carry out numerical simulations that illustrate its ability to reach consensus effectively while ensuring the protection of private information.展开更多
In the era of big data,the growing number of real-time data streams often contains a lot of sensitive privacy information.Releasing or sharing this data directly without processing will lead to serious privacy informa...In the era of big data,the growing number of real-time data streams often contains a lot of sensitive privacy information.Releasing or sharing this data directly without processing will lead to serious privacy information leakage.This poses a great challenge to conventional privacy protection mechanisms(CPPM).The existing data partitioning methods ignore the number of data replications and information exchanges,resulting in complex distance calculations and inefficient indexing for high-dimensional data.Therefore,CPPM often fails to meet the stringent requirements of efficiency and reliability,especially in dynamic spatiotemporal environments.Addressing this concern,we proposed the Principal Component Enhanced Vantage-point tree(PEV-Tree),which is an enhanced data structure based on the idea of dimension reduction,and constructed a Distributed Spatio-Temporal Privacy Preservation Mechanism(DST-PPM)on it.In this work,principal component analysis and the vantage tree are used to establish the PEV-Tree.In addition,we designed three distributed anonymization algorithms for data streams.These algorithms are named CK-AA,CL-DA,and CT-CA,fulfill the anonymization rules of K-Anonymity,L-Diversity,and T-Closeness,respectively,which have different computational complexities and reliabilities.The higher the complexity,the lower the risk of privacy leakage.DST-PPM can reduce the dimension of high-dimensional information while preserving data characteristics and dividing the data space into vantage points based on distance.It effectively enhances the data processing workflow and increases algorithmefficiency.To verify the validity of the method in this paper,we conducted empirical tests of CK-AA,CL-DA,and CT-CA on conventional datasets and the PEV-Tree,respectively.Based on the big data background of the Internet of Vehicles,we conducted experiments using artificial simulated on-board network data.The results demonstrated that the operational efficiency of the CK-AA,CL-DA,and CT-CA is enhanced by 15.12%,24.55%,and 52.74%,respectively,when deployed on the PEV-Tree.Simultaneously,during homogeneity attacks,the probabilities of information leakage were reduced by 2.31%,1.76%,and 0.19%,respectively.Furthermore,these algorithms showcased superior utility(scalability)when executed across PEV-Trees of varying scales in comparison to their performance on conventional data structures.It indicates that DST-PPM offers marked advantages over CPPM in terms of efficiency,reliability,and scalability.展开更多
Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.P...Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.展开更多
In this study,we investigated privacy-preserving ID3 Decision Tree(PPID3)training and inference based on fully homomorphic encryption(FHE),which has not been actively explored due to the high computational cost associ...In this study,we investigated privacy-preserving ID3 Decision Tree(PPID3)training and inference based on fully homomorphic encryption(FHE),which has not been actively explored due to the high computational cost associated with managing numerous child nodes in an ID3 tree.We propose HEaaN-ID3,a novel approach to realize PPID3 using the Cheon-Kim-Kim-Song(CKKS)scheme.HEaaN-ID3 is the first FHE-based ID3 framework that completes both training and inference without any intermediate decryption,which is especially valuable when decryption keys are inaccessible or a single-cloud security domain is assumed.To enhance computational efficiency,we adopt a modified Gini impurity(MGI)score instead of entropy to evaluate information gain,thereby avoiding costly inverse operations.In addition,we fully leverage the Single Instruction Multiple Data(SIMD)property of CKKS to parallelize computations at multiple tree nodes.Unlike previous approaches that require decryption at each node or rely on two-party secure computation,our method enables a fully non-interactive training and inference pipeline in the encrypted domain.We validated the proposed scheme using UCI datasets with both numerical and nominal features,demonstrating inference accuracy comparable to plaintext implementations in Scikit-Learn.Moreover,experiments show that HEaaN-ID3 significantly reduces training and inference time per node relative to earlier FHE-based approaches.展开更多
The development and deployment of privary preserving supply chain quantity discount contract design can allow supply chain collaborations to take place without revealing any participant＇s data to others, reaping the ...The development and deployment of privary preserving supply chain quantity discount contract design can allow supply chain collaborations to take place without revealing any participant＇s data to others, reaping the benefits of collaborations wbile avoiding the drawbacks of privacy information disclosure. First, secure multi-party computation protocols are applied in the joint-ordering policy between a single supplier and a single retailer, the joint-ordering policy can be conducted without disclosing private cost information of any of the other supply chain partners. Secondly, secure multi-party computation protocols are applied in the privacy preserving supply chain quantity discount contract design between a single supplier and a single retailer. The information disclosure analyses of the algorithm show that： the optimal quantity discount of the jointordering policy can be conducted without disclosing private cost information of any of the other supply chain partners; the above protocol can be implemented without mediators; the privacy preserving quantity discount algorithm can be mutually verifiable and has solved the problem of asymmetric information.展开更多
Identity-Based Encryption (IBE) has seen limited adoption, largely due to the absolute trust that must be placed in the private key generator (PKG)—an authority that computes the private keys for all the users in the...Identity-Based Encryption (IBE) has seen limited adoption, largely due to the absolute trust that must be placed in the private key generator (PKG)—an authority that computes the private keys for all the users in the environment. Several constructions have been proposed to reduce the trust required in the PKG (and thus preserve the privacy of users), but these have generally relied on unrealistic assumptions regarding non-collusion between various entities in the system. Unfortunately, these constructions have not significantly improved IBE adoption rates in real-world environments. In this paper, we present a construction that reduces trust in the PKG without unrealistic non-collusion assumptions. We achieve this by incorporating a novel combination of digital credential technology and bilinear maps, and making use of multiple randomly-chosen entities to complete certain tasks. The main result and primary contribution of this paper are a thorough security analysis of this proposed construction, examining the various entity types, attacker models, and collusion opportunities in this environment. We show that this construction can prevent, or at least mitigate, all considered attacks. We conclude that our construction appears to be effective in preserving user privacy and we hope that this construction and its security analysis will encourage greater use of IBE in real-world environments.展开更多
The fast proliferation of edge devices for the Internet of Things(IoT)has led to massive volumes of data explosion.The generated data is collected and shared using edge-based IoT structures at a considerably high freq...The fast proliferation of edge devices for the Internet of Things(IoT)has led to massive volumes of data explosion.The generated data is collected and shared using edge-based IoT structures at a considerably high frequency.Thus,the data-sharing privacy exposure issue is increasingly intimidating when IoT devices make malicious requests for filching sensitive information from a cloud storage system through edge nodes.To address the identified issue,we present evolutionary privacy preservation learning strategies for an edge computing-based IoT data sharing scheme.In particular,we introduce evolutionary game theory and construct a payoff matrix to symbolize intercommunication between IoT devices and edge nodes,where IoT devices and edge nodes are two parties of the game.IoT devices may make malicious requests to achieve their goals of stealing privacy.Accordingly,edge nodes should deny malicious IoT device requests to prevent IoT data from being disclosed.They dynamically adjust their own strategies according to the opponent's strategy and finally maximize the payoffs.Built upon a developed application framework to illustrate the concrete data sharing architecture,a novel algorithm is proposed that can derive the optimal evolutionary learning strategy.Furthermore,we numerically simulate evolutionarily stable strategies,and the final results experimentally verify the correctness of the IoT data sharing privacy preservation scheme.Therefore,the proposed model can effectively defeat malicious invasion and protect sensitive information from leaking when IoT data is shared.展开更多
Location estimation of underwater sensor networks(USNs)has become a critical technology,due to its fundamental role in the sensing,communication and control of ocean volume.However,the asynchronous clock,security atta...Location estimation of underwater sensor networks(USNs)has become a critical technology,due to its fundamental role in the sensing,communication and control of ocean volume.However,the asynchronous clock,security attack and mobility characteristics of underwater environment make localization much more challenging as compared with terrestrial sensor networks.This paper is concerned with a privacy-preserving asynchronous localization issue for USNs.Particularly,a hybrid network architecture that includes surface buoys,anchor nodes,active sensor nodes and ordinary sensor nodes is constructed.Then,an asynchronous localization protocol is provided,through which two privacy-preserving localization algorithms are designed to estimate the locations of active and ordinary sensor nodes.It is worth mentioning that,the proposed localization algorithms reveal disguised positions to the network,while they do not adopt any homomorphic encryption technique.More importantly,they can eliminate the effect of asynchronous clock,i.e.,clock skew and offset.The performance analyses for the privacy-preserving asynchronous localization algorithms are also presented.Finally,simulation and experiment results reveal that the proposed localization approach can avoid the leakage of position information,while the location accuracy can be significantly enhanced as compared with the other works.展开更多
Cross-Domain Recommendation(CDR)aims to solve data sparsity and cold-start problems by utilizing a relatively information-rich source domain to improve the recommendation performance of the data-sparse target domain.H...Cross-Domain Recommendation(CDR)aims to solve data sparsity and cold-start problems by utilizing a relatively information-rich source domain to improve the recommendation performance of the data-sparse target domain.However,most existing approaches rely on the assumption of centralized storage of user data,which undoubtedly poses a significant risk of user privacy leakage because user data are highly privacy-sensitive.To this end,we propose a privacy-preserving Federated framework for Cross-Domain Recommendation,called FedCDR.In our method,to avoid leakage of user privacy,a general recommendation model is trained on each user's personal device to obtain embeddings of users and items,and each client uploads weights to the central server.The central server then aggregates the weights and distributes them to each client for updating.Furthermore,because the weights implicitly contain private information about the user,local differential privacy is adopted for the gradients before uploading them to the server for better protection of user privacy.To distill the relationship of user embedding between two domains,an embedding transformation mechanism is used on the server side to learn the cross-domain embedding transformation model.Extensive experiments on real-world datasets demonstrate that ourmethod achieves performance comparable with that of existing data-centralized methods and effectively protects user privacy.展开更多
Collaborative filtering(CF)methods are widely adopted by existing medical recommendation systems,which can help clinicians perform their work by seeking and recommending appropriate medical advice.However,privacy issu...Collaborative filtering(CF)methods are widely adopted by existing medical recommendation systems,which can help clinicians perform their work by seeking and recommending appropriate medical advice.However,privacy issue arises in this process as sensitive patient private data are collected by the recommendation server.Recently proposed privacy-preserving collaborative filtering methods,using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in medical online service.The aim of this study is to address the privacy issues in the context of neighborhoodbased CF methods by proposing a Privacy Preserving Medical Recommendation(PPMR)algorithm,which can protect patients’treatment information and demographic information during online recommendation process without compromising recommendation accuracy and efficiency.The proposed algorithm includes two privacy preserving operations:Private Neighbor Selection and Neighborhood-based Differential Privacy Recommendation.Private Neighbor Selection is conducted on the basis of the notion of k-anonymity method,meaning that neighbors are privately selected for the target user according to his/her similarities with others.Neighborhood-based Differential Privacy Recommendation and a differential privacy mechanism are introduced in this operation to enhance the performance of recommendation.Our algorithm is evaluated using the real-world hospital EMRs dataset.Experimental results demonstrate that the proposed method achieves stable recommendation accuracy while providing comprehensive privacy for individual patients.展开更多
Latest developments in computing and communication technologies are enabled the design of connected healthcare system which are mainly based on IoT and Edge technologies.Blockchain,data encryption,and deep learning(DL...Latest developments in computing and communication technologies are enabled the design of connected healthcare system which are mainly based on IoT and Edge technologies.Blockchain,data encryption,and deep learning(DL)models can be utilized to design efficient security solutions for IoT healthcare applications.In this aspect,this article introduces a Blockchain with privacy preserving image encryption and optimal deep learning(BPPIEODL)technique for IoT healthcare applications.The proposed BPPIE-ODL technique intends to securely transmit the encrypted medical images captured by IoT devices and performs classification process at the cloud server.The proposed BPPIE-ODL technique encompasses the design of dragonfly algorithm(DFA)with signcryption technique to encrypt the medical images captured by the IoT devices.Besides,blockchain(BC)can be utilized as a distributed data saving approach for generating a ledger,which permits access to the users and prevents third party’s access to encrypted data.In addition,the classification process includes SqueezeNet based feature extraction,softmax classifier(SMC),and Nadam based hyperparameter optimizer.The usage of Nadam model helps to optimally regulate the hyperparameters of the SqueezeNet architecture.For examining the enhanced encryption as well as classification performance of the BPPIE-ODL technique,a comprehensive experimental analysis is carried out.The simulation outcomes demonstrate the significant performance of the BPPIE-ODL technique on the other techniques with increased precision and accuracy of 0.9551 and 0.9813 respectively.展开更多
Due to mobile Internet technology's rapid popularization,the Industrial Internet of Things(IIoT)can be seen everywhere in our daily lives.While IIoT brings us much convenience,a series of security and scalability ...Due to mobile Internet technology's rapid popularization,the Industrial Internet of Things(IIoT)can be seen everywhere in our daily lives.While IIoT brings us much convenience,a series of security and scalability issues related to permission operations rise to the surface during device communications.Hence,at present,a reliable and dynamic access control management system for IIoT is in urgent need.Up till now,numerous access control architectures have been proposed for IIoT.However,owing to centralized models and heterogeneous devices,security and scalability requirements still cannot be met.In this paper,we offer a smart contract token-based solution for decentralized access control in IIoT systems.Specifically,there are three smart contracts in our system,including the Token Issue Contract(TIC),User Register Contract(URC),and Manage Contract(MC).These three contracts collaboratively supervise and manage various events in IIoT environments.We also utilize the lightweight and post-quantum encryption algorithm-Nth-degree Truncated Polynomial Ring Units(NTRU)to preserve user privacy during the registration process.Subsequently,to evaluate our proposed architecture's performance,we build a prototype platform that connects to the local blockchain.Finally,experiment results show that our scheme has achieved secure and dynamic access control for the IIoT system compared with related research.展开更多
Air pollution has become a global concern for many years.Vehicular crowdsensing systems make it possible to monitor air quality at a fine granularity.To better utilize the sensory data with varying credibility,truth d...Air pollution has become a global concern for many years.Vehicular crowdsensing systems make it possible to monitor air quality at a fine granularity.To better utilize the sensory data with varying credibility,truth discovery frameworks are introduced.However,in urban cities,there is a significant difference in traffic volumes of streets or blocks,which leads to a data sparsity problem for truth discovery.Protecting the privacy of participant vehicles is also a crucial task.We first present a data masking-based privacy-preserving truth discovery framework,which incorporates spatial and temporal correlations to solve the sparsity problem.To further improve the truth discovery performance of the presented framework,an enhanced version is proposed with anonymous communication and data perturbation.Both frameworks are more lightweight than the existing cryptography-based methods.We also evaluate the work with simulations and fully discuss the performance and possible extensions.展开更多
As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in dat...As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in data fusion,low reliability of data storage,low effectiveness of data sharing,etc.To guarantee the service quality of data collaboration,this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning,termed FL-HMChain.This system is composed of three layers:Data extraction and storage,data management,and data application.Focusing on healthcare and medical data,a healthcare and medical blockchain is constructed to realize data storage,transfer,processing,and access with security,real-time,reliability,and integrity.An improved master node selection consensus mechanism is presented to detect and prevent dishonest behavior,ensuring the overall reliability and trustworthiness of the collaborative model training process.Furthermore,healthcare and medical data collaboration services in real-world scenarios have been discussed and developed.To further validate the performance of FL-HMChain,a Convolutional Neural Network-based Federated Learning(FL-CNN-HMChain)model is investigated for medical image identification.This model achieves better performance compared to the baseline Convolutional Neural Network(CNN),having an average improvement of 4.7%on Area Under Curve(AUC)and 7%on Accuracy(ACC),respectively.Furthermore,the probability of privacy leakage can be effectively reduced by the blockchain-based parameter transfer mechanism in federated learning between local and global models.展开更多
Demand response has been intensively studied in recent years. It can motivate customers to change their consumption patterns according to the dynamic(time-varying) electricity price, which is considered to be the most...Demand response has been intensively studied in recent years. It can motivate customers to change their consumption patterns according to the dynamic(time-varying) electricity price, which is considered to be the most cost-effective and reliable solution for smoothing the demand curve. However, many existing schemes, based on users' demand request in each period, require users to consume their requested electricity exactly, which sometimes causes inconvenience and losses to the utility, because customers cannot always be able to consume the accurate electricity demand due to various personal reasons. In this paper, we tackle this problem in a novel approach. Instead of charging after consumption, we adopt the prepayment mechanism to implement power request. Furthermore, we propose a trading market running by the control center to cope with the users' dynamic demand. It is noteworthy that both users' original demand and trading records are protected against potential adversaries including the curious control center. Through the numerical simulation, we demonstrate that our scheme is highly efficient in both computation and communication.展开更多
The concept of Internet of Everything is like a revolutionary storm,bringing the whole society closer together.Internet of Things(IoT)has played a vital role in the process.With the rise of the concept of Industry 4.0...The concept of Internet of Everything is like a revolutionary storm,bringing the whole society closer together.Internet of Things(IoT)has played a vital role in the process.With the rise of the concept of Industry 4.0,intelligent transformation is taking place in the industrial field.As a new concept,an industrial IoT system has also attracted the attention of industry and academia.In an actual industrial scenario,a large number of devices will generate numerous industrial datasets.The computing efficiency of an industrial IoT system is greatly improved with the help of using either cloud computing or edge computing.However,privacy issues may seriously harmed interests of users.In this article,we summarize privacy issues in a cloud-or an edge-based industrial IoT system.The privacy analysis includes data privacy,location privacy,query and identity privacy.In addition,we also review privacy solutions when applying software defined network and blockchain under the above two systems.Next,we analyze the computational complexity and privacy protection performance of these solutions.Finally,we discuss open issues to facilitate further studies.展开更多
Recently,the application of Wireless Sensor Networks(WSNs)has been increasing rapidly.It requires privacy preserving data aggregation protocols to secure the data from compromises.Preserving privacy of the sensor data...Recently,the application of Wireless Sensor Networks(WSNs)has been increasing rapidly.It requires privacy preserving data aggregation protocols to secure the data from compromises.Preserving privacy of the sensor data is a challenging task.This paper presents a non-linear regression-based data aggregation protocol for preserving privacy of the sensor data.The proposed protocol uses non-linear regression functions to represent the sensor data collected from the sensor nodes.Instead of sending the complete data to the cluster head,the sensor nodes only send the coefficients of the non-linear function.This will reduce the communication overhead of the network.The data aggregation is performed on the masked coefficients and the sink node is able to retrieve the approximated results over the aggregated data.The analysis of experiment results shows that the proposed protocol is able to minimize communication overhead,enhance data aggregation accuracy,and preserve data privacy.展开更多
In this paper,we study the problem of privacy-preserving top-k keyword similarity search over outsourced cloud data.Taking edit distance as a measure of similarity,we first build up the similarity keyword sets for all...In this paper,we study the problem of privacy-preserving top-k keyword similarity search over outsourced cloud data.Taking edit distance as a measure of similarity,we first build up the similarity keyword sets for all the keywords in the data collection.We then calculate the relevance scores of the elements in the similarity keyword sets by the widely used tf-idf theory.Leveraging both the similarity keyword sets and the relevance scores,we present a new secure and efficient treebased index structure for privacy-preserving top-k keyword similarity search.To prevent potential statistical attacks,we also introduce a two-server model to separate the association between the index structure and the data collection in cloud servers.Thorough analysis is given on the validity of search functionality and formal security proofs are presented for the privacy guarantee of our solution.Experimental results on real-world data sets further demonstrate the availability and efficiency of our solution.展开更多
With the increasing development of smart grid,multi-party cooperative computation between several entities has become a typical characteristic of modern energy systems.Traditionally,data exchange among parties is inev...With the increasing development of smart grid,multi-party cooperative computation between several entities has become a typical characteristic of modern energy systems.Traditionally,data exchange among parties is inevitable,rendering how to complete multi-party collaborative optimization without exposing any private information a critical issue.This paper proposes a fully privacy-preserving distributed optimization framework based on secure multi-party computation(SMPC)with secret sharing protocols.The framework decomposes the collaborative optimization problem into a master problem and several subproblems.The process of solving the master problem is executed in the SMPC framework via the secret sharing protocols among agents.The relationships of agents are completely equal,and there is no privileged agent or any third party.The process of solving subproblems is conducted by agents individually.Compared to the traditional distributed optimization framework,the proposed SMPC-based framework can fully preserve individual private information.Exchanged data among agents are encrypted and no private information disclosure is assured.Furthermore,the framework maintains a limited and acceptable increase in computational costs while guaranteeing opti-mality.Case studies are conducted on test systems of different scales to demonstrate the principle of secret sharing and verify the feasibility and scalability of the proposed methodology.展开更多
This paper presents a novel privacy principle, ε-inclusion, for re-publishing sensitive dynamic datasets. ε-inclusion releases all the quasi-identifier values directly and uses permutation-based method and substitut...This paper presents a novel privacy principle, ε-inclusion, for re-publishing sensitive dynamic datasets. ε-inclusion releases all the quasi-identifier values directly and uses permutation-based method and substitution to anonymize the microdata. Combined with generalization-based methods, ε-inclusion protects privacy and captures a large amount of correlation in the microdata. We develop an effective algorithm for computing anonymized tables that obey the ε-inclusion privacy requirement. Extensive experiments confirm that our solution allows significantly more effective data analysis than generalization-based methods.展开更多
基金supported by National Natural Science Foundation of China under Grants 62203045,62433020 and T2293770。
文摘The issue of privacy leakage in distributed consensus has garnered significant attention over the years,but existing studies often overlook the challenges posed by limited communication in algorithm design.This paper addresses the issue of privacy preservation in distributed weighted average consensus under limited communication scenarios.Specifically targeting directed and unbalanced topologies,we propose a privacy-preserving implementation protocol that incorporates the Paillier homomorphic encryption scheme.The protocol encrypts only the 1-bit quantized messages exchanged between agents,thus ensuring both the correctness of the consensus result and the confidentiality of each agent's initial state.To demonstrate the practicality of the proposed method,we carry out numerical simulations that illustrate its ability to reach consensus effectively while ensuring the protection of private information.
基金supported by the Natural Science Foundation of Sichuan Province(No.2024NSFSC1450)the Fundamental Research Funds for the Central Universities(No.SCU2024D012)the Science and Engineering Connotation Development Project of Sichuan University(No.2020SCUNG129).
文摘In the era of big data,the growing number of real-time data streams often contains a lot of sensitive privacy information.Releasing or sharing this data directly without processing will lead to serious privacy information leakage.This poses a great challenge to conventional privacy protection mechanisms(CPPM).The existing data partitioning methods ignore the number of data replications and information exchanges,resulting in complex distance calculations and inefficient indexing for high-dimensional data.Therefore,CPPM often fails to meet the stringent requirements of efficiency and reliability,especially in dynamic spatiotemporal environments.Addressing this concern,we proposed the Principal Component Enhanced Vantage-point tree(PEV-Tree),which is an enhanced data structure based on the idea of dimension reduction,and constructed a Distributed Spatio-Temporal Privacy Preservation Mechanism(DST-PPM)on it.In this work,principal component analysis and the vantage tree are used to establish the PEV-Tree.In addition,we designed three distributed anonymization algorithms for data streams.These algorithms are named CK-AA,CL-DA,and CT-CA,fulfill the anonymization rules of K-Anonymity,L-Diversity,and T-Closeness,respectively,which have different computational complexities and reliabilities.The higher the complexity,the lower the risk of privacy leakage.DST-PPM can reduce the dimension of high-dimensional information while preserving data characteristics and dividing the data space into vantage points based on distance.It effectively enhances the data processing workflow and increases algorithmefficiency.To verify the validity of the method in this paper,we conducted empirical tests of CK-AA,CL-DA,and CT-CA on conventional datasets and the PEV-Tree,respectively.Based on the big data background of the Internet of Vehicles,we conducted experiments using artificial simulated on-board network data.The results demonstrated that the operational efficiency of the CK-AA,CL-DA,and CT-CA is enhanced by 15.12%,24.55%,and 52.74%,respectively,when deployed on the PEV-Tree.Simultaneously,during homogeneity attacks,the probabilities of information leakage were reduced by 2.31%,1.76%,and 0.19%,respectively.Furthermore,these algorithms showcased superior utility(scalability)when executed across PEV-Trees of varying scales in comparison to their performance on conventional data structures.It indicates that DST-PPM offers marked advantages over CPPM in terms of efficiency,reliability,and scalability.
基金supported by Natural Science Foundation of China(Nos.62303126,62362008,author Z.Z,https://www.nsfc.gov.cn/,accessed on 20 December 2024)Major Scientific and Technological Special Project of Guizhou Province([2024]014)+2 种基金Guizhou Provincial Science and Technology Projects(No.ZK[2022]General149) ,author Z.Z,https://kjt.guizhou.gov.cn/,accessed on 20 December 2024)The Open Project of the Key Laboratory of Computing Power Network and Information Security,Ministry of Education under Grant 2023ZD037,author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024)Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2024B25),author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024).
文摘Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.
基金supported by Institute of Information communications Technology Planning Evaluation(IITP)grant funded by theKorea government(MSIT)[No.2022-0-01047,Development of statistical analysis algorithm and module using homomorphic encryption based on real number operation,100%].
文摘In this study,we investigated privacy-preserving ID3 Decision Tree(PPID3)training and inference based on fully homomorphic encryption(FHE),which has not been actively explored due to the high computational cost associated with managing numerous child nodes in an ID3 tree.We propose HEaaN-ID3,a novel approach to realize PPID3 using the Cheon-Kim-Kim-Song(CKKS)scheme.HEaaN-ID3 is the first FHE-based ID3 framework that completes both training and inference without any intermediate decryption,which is especially valuable when decryption keys are inaccessible or a single-cloud security domain is assumed.To enhance computational efficiency,we adopt a modified Gini impurity(MGI)score instead of entropy to evaluate information gain,thereby avoiding costly inverse operations.In addition,we fully leverage the Single Instruction Multiple Data(SIMD)property of CKKS to parallelize computations at multiple tree nodes.Unlike previous approaches that require decryption at each node or rely on two-party secure computation,our method enables a fully non-interactive training and inference pipeline in the encrypted domain.We validated the proposed scheme using UCI datasets with both numerical and nominal features,demonstrating inference accuracy comparable to plaintext implementations in Scikit-Learn.Moreover,experiments show that HEaaN-ID3 significantly reduces training and inference time per node relative to earlier FHE-based approaches.
基金The National Natural Science Foundation of China(No.70771026)
文摘The development and deployment of privary preserving supply chain quantity discount contract design can allow supply chain collaborations to take place without revealing any participant＇s data to others, reaping the benefits of collaborations wbile avoiding the drawbacks of privacy information disclosure. First, secure multi-party computation protocols are applied in the joint-ordering policy between a single supplier and a single retailer, the joint-ordering policy can be conducted without disclosing private cost information of any of the other supply chain partners. Secondly, secure multi-party computation protocols are applied in the privacy preserving supply chain quantity discount contract design between a single supplier and a single retailer. The information disclosure analyses of the algorithm show that： the optimal quantity discount of the jointordering policy can be conducted without disclosing private cost information of any of the other supply chain partners; the above protocol can be implemented without mediators; the privacy preserving quantity discount algorithm can be mutually verifiable and has solved the problem of asymmetric information.
文摘Identity-Based Encryption (IBE) has seen limited adoption, largely due to the absolute trust that must be placed in the private key generator (PKG)—an authority that computes the private keys for all the users in the environment. Several constructions have been proposed to reduce the trust required in the PKG (and thus preserve the privacy of users), but these have generally relied on unrealistic assumptions regarding non-collusion between various entities in the system. Unfortunately, these constructions have not significantly improved IBE adoption rates in real-world environments. In this paper, we present a construction that reduces trust in the PKG without unrealistic non-collusion assumptions. We achieve this by incorporating a novel combination of digital credential technology and bilinear maps, and making use of multiple randomly-chosen entities to complete certain tasks. The main result and primary contribution of this paper are a thorough security analysis of this proposed construction, examining the various entity types, attacker models, and collusion opportunities in this environment. We show that this construction can prevent, or at least mitigate, all considered attacks. We conclude that our construction appears to be effective in preserving user privacy and we hope that this construction and its security analysis will encourage greater use of IBE in real-world environments.
基金supported in part by Zhejiang Provincial Natural Science Foundation of China under Grant nos.LZ22F020002 and LY22F020003National Natural Science Foundation of China under Grant nos.61772018 and 62002226the key project of Humanities and Social Sciences in Colleges and Universities of Zhejiang Province under Grant no.2021GH017.
文摘The fast proliferation of edge devices for the Internet of Things(IoT)has led to massive volumes of data explosion.The generated data is collected and shared using edge-based IoT structures at a considerably high frequency.Thus,the data-sharing privacy exposure issue is increasingly intimidating when IoT devices make malicious requests for filching sensitive information from a cloud storage system through edge nodes.To address the identified issue,we present evolutionary privacy preservation learning strategies for an edge computing-based IoT data sharing scheme.In particular,we introduce evolutionary game theory and construct a payoff matrix to symbolize intercommunication between IoT devices and edge nodes,where IoT devices and edge nodes are two parties of the game.IoT devices may make malicious requests to achieve their goals of stealing privacy.Accordingly,edge nodes should deny malicious IoT device requests to prevent IoT data from being disclosed.They dynamically adjust their own strategies according to the opponent's strategy and finally maximize the payoffs.Built upon a developed application framework to illustrate the concrete data sharing architecture,a novel algorithm is proposed that can derive the optimal evolutionary learning strategy.Furthermore,we numerically simulate evolutionarily stable strategies,and the final results experimentally verify the correctness of the IoT data sharing privacy preservation scheme.Therefore,the proposed model can effectively defeat malicious invasion and protect sensitive information from leaking when IoT data is shared.
基金supported in part by the National Natural Science Foundation of China(61873345,61973263)the Youth Talent Support Program of Hebei(BJ2018050,BJ2020031)+2 种基金the Teturned Overseas Chinese Scholar Foundation of Hebei(C201829)the Natural Science Foundation of Hebei(F2020203002)the Postgraduate Innovation Fund Project of Hebei(CXZZSS2019047)。
文摘Location estimation of underwater sensor networks(USNs)has become a critical technology,due to its fundamental role in the sensing,communication and control of ocean volume.However,the asynchronous clock,security attack and mobility characteristics of underwater environment make localization much more challenging as compared with terrestrial sensor networks.This paper is concerned with a privacy-preserving asynchronous localization issue for USNs.Particularly,a hybrid network architecture that includes surface buoys,anchor nodes,active sensor nodes and ordinary sensor nodes is constructed.Then,an asynchronous localization protocol is provided,through which two privacy-preserving localization algorithms are designed to estimate the locations of active and ordinary sensor nodes.It is worth mentioning that,the proposed localization algorithms reveal disguised positions to the network,while they do not adopt any homomorphic encryption technique.More importantly,they can eliminate the effect of asynchronous clock,i.e.,clock skew and offset.The performance analyses for the privacy-preserving asynchronous localization algorithms are also presented.Finally,simulation and experiment results reveal that the proposed localization approach can avoid the leakage of position information,while the location accuracy can be significantly enhanced as compared with the other works.
基金supported by the Key Project of Nature Science Research for the Universities of Anhui Province of China(No.KJ2020A0657)the National Science Foundation of China(No.61872002)the Key Research and Development Program of Anhui Province(No.202104a05020058).
文摘Cross-Domain Recommendation(CDR)aims to solve data sparsity and cold-start problems by utilizing a relatively information-rich source domain to improve the recommendation performance of the data-sparse target domain.However,most existing approaches rely on the assumption of centralized storage of user data,which undoubtedly poses a significant risk of user privacy leakage because user data are highly privacy-sensitive.To this end,we propose a privacy-preserving Federated framework for Cross-Domain Recommendation,called FedCDR.In our method,to avoid leakage of user privacy,a general recommendation model is trained on each user's personal device to obtain embeddings of users and items,and each client uploads weights to the central server.The central server then aggregates the weights and distributes them to each client for updating.Furthermore,because the weights implicitly contain private information about the user,local differential privacy is adopted for the gradients before uploading them to the server for better protection of user privacy.To distill the relationship of user embedding between two domains,an embedding transformation mechanism is used on the server side to learn the cross-domain embedding transformation model.Extensive experiments on real-world datasets demonstrate that ourmethod achieves performance comparable with that of existing data-centralized methods and effectively protects user privacy.
文摘Collaborative filtering(CF)methods are widely adopted by existing medical recommendation systems,which can help clinicians perform their work by seeking and recommending appropriate medical advice.However,privacy issue arises in this process as sensitive patient private data are collected by the recommendation server.Recently proposed privacy-preserving collaborative filtering methods,using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in medical online service.The aim of this study is to address the privacy issues in the context of neighborhoodbased CF methods by proposing a Privacy Preserving Medical Recommendation(PPMR)algorithm,which can protect patients’treatment information and demographic information during online recommendation process without compromising recommendation accuracy and efficiency.The proposed algorithm includes two privacy preserving operations:Private Neighbor Selection and Neighborhood-based Differential Privacy Recommendation.Private Neighbor Selection is conducted on the basis of the notion of k-anonymity method,meaning that neighbors are privately selected for the target user according to his/her similarities with others.Neighborhood-based Differential Privacy Recommendation and a differential privacy mechanism are introduced in this operation to enhance the performance of recommendation.Our algorithm is evaluated using the real-world hospital EMRs dataset.Experimental results demonstrate that the proposed method achieves stable recommendation accuracy while providing comprehensive privacy for individual patients.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP.1/283/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R136),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Latest developments in computing and communication technologies are enabled the design of connected healthcare system which are mainly based on IoT and Edge technologies.Blockchain,data encryption,and deep learning(DL)models can be utilized to design efficient security solutions for IoT healthcare applications.In this aspect,this article introduces a Blockchain with privacy preserving image encryption and optimal deep learning(BPPIEODL)technique for IoT healthcare applications.The proposed BPPIE-ODL technique intends to securely transmit the encrypted medical images captured by IoT devices and performs classification process at the cloud server.The proposed BPPIE-ODL technique encompasses the design of dragonfly algorithm(DFA)with signcryption technique to encrypt the medical images captured by the IoT devices.Besides,blockchain(BC)can be utilized as a distributed data saving approach for generating a ledger,which permits access to the users and prevents third party’s access to encrypted data.In addition,the classification process includes SqueezeNet based feature extraction,softmax classifier(SMC),and Nadam based hyperparameter optimizer.The usage of Nadam model helps to optimally regulate the hyperparameters of the SqueezeNet architecture.For examining the enhanced encryption as well as classification performance of the BPPIE-ODL technique,a comprehensive experimental analysis is carried out.The simulation outcomes demonstrate the significant performance of the BPPIE-ODL technique on the other techniques with increased precision and accuracy of 0.9551 and 0.9813 respectively.
文摘Due to mobile Internet technology's rapid popularization,the Industrial Internet of Things(IIoT)can be seen everywhere in our daily lives.While IIoT brings us much convenience,a series of security and scalability issues related to permission operations rise to the surface during device communications.Hence,at present,a reliable and dynamic access control management system for IIoT is in urgent need.Up till now,numerous access control architectures have been proposed for IIoT.However,owing to centralized models and heterogeneous devices,security and scalability requirements still cannot be met.In this paper,we offer a smart contract token-based solution for decentralized access control in IIoT systems.Specifically,there are three smart contracts in our system,including the Token Issue Contract(TIC),User Register Contract(URC),and Manage Contract(MC).These three contracts collaboratively supervise and manage various events in IIoT environments.We also utilize the lightweight and post-quantum encryption algorithm-Nth-degree Truncated Polynomial Ring Units(NTRU)to preserve user privacy during the registration process.Subsequently,to evaluate our proposed architecture's performance,we build a prototype platform that connects to the local blockchain.Finally,experiment results show that our scheme has achieved secure and dynamic access control for the IIoT system compared with related research.
文摘Air pollution has become a global concern for many years.Vehicular crowdsensing systems make it possible to monitor air quality at a fine granularity.To better utilize the sensory data with varying credibility,truth discovery frameworks are introduced.However,in urban cities,there is a significant difference in traffic volumes of streets or blocks,which leads to a data sparsity problem for truth discovery.Protecting the privacy of participant vehicles is also a crucial task.We first present a data masking-based privacy-preserving truth discovery framework,which incorporates spatial and temporal correlations to solve the sparsity problem.To further improve the truth discovery performance of the presented framework,an enhanced version is proposed with anonymous communication and data perturbation.Both frameworks are more lightweight than the existing cryptography-based methods.We also evaluate the work with simulations and fully discuss the performance and possible extensions.
基金We are thankful for the funding support fromthe Science and Technology Projects of the National Archives Administration of China(Grant Number 2022-R-031)the Fundamental Research Funds for the Central Universities,Central China Normal University(Grant Number CCNU24CG014).
文摘As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in data fusion,low reliability of data storage,low effectiveness of data sharing,etc.To guarantee the service quality of data collaboration,this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning,termed FL-HMChain.This system is composed of three layers:Data extraction and storage,data management,and data application.Focusing on healthcare and medical data,a healthcare and medical blockchain is constructed to realize data storage,transfer,processing,and access with security,real-time,reliability,and integrity.An improved master node selection consensus mechanism is presented to detect and prevent dishonest behavior,ensuring the overall reliability and trustworthiness of the collaborative model training process.Furthermore,healthcare and medical data collaboration services in real-world scenarios have been discussed and developed.To further validate the performance of FL-HMChain,a Convolutional Neural Network-based Federated Learning(FL-CNN-HMChain)model is investigated for medical image identification.This model achieves better performance compared to the baseline Convolutional Neural Network(CNN),having an average improvement of 4.7%on Area Under Curve(AUC)and 7%on Accuracy(ACC),respectively.Furthermore,the probability of privacy leakage can be effectively reduced by the blockchain-based parameter transfer mechanism in federated learning between local and global models.
基金supported by the National Key Research and Development Plan of China under Grant No.2016YFB0800301the Fund of Science and Technology on Communication Networks Laboratory under Grant No.KX162600024Youth Innovation Promotion Association CAS under Grant No.2016394
文摘Demand response has been intensively studied in recent years. It can motivate customers to change their consumption patterns according to the dynamic(time-varying) electricity price, which is considered to be the most cost-effective and reliable solution for smoothing the demand curve. However, many existing schemes, based on users' demand request in each period, require users to consume their requested electricity exactly, which sometimes causes inconvenience and losses to the utility, because customers cannot always be able to consume the accurate electricity demand due to various personal reasons. In this paper, we tackle this problem in a novel approach. Instead of charging after consumption, we adopt the prepayment mechanism to implement power request. Furthermore, we propose a trading market running by the control center to cope with the users' dynamic demand. It is noteworthy that both users' original demand and trading records are protected against potential adversaries including the curious control center. Through the numerical simulation, we demonstrate that our scheme is highly efficient in both computation and communication.
基金the National Natural Science Foundation of China(Grant No.61871023 and 61931001)Beijing Natural Science Foundation(Grant No.4202054).
文摘The concept of Internet of Everything is like a revolutionary storm,bringing the whole society closer together.Internet of Things(IoT)has played a vital role in the process.With the rise of the concept of Industry 4.0,intelligent transformation is taking place in the industrial field.As a new concept,an industrial IoT system has also attracted the attention of industry and academia.In an actual industrial scenario,a large number of devices will generate numerous industrial datasets.The computing efficiency of an industrial IoT system is greatly improved with the help of using either cloud computing or edge computing.However,privacy issues may seriously harmed interests of users.In this article,we summarize privacy issues in a cloud-or an edge-based industrial IoT system.The privacy analysis includes data privacy,location privacy,query and identity privacy.In addition,we also review privacy solutions when applying software defined network and blockchain under the above two systems.Next,we analyze the computational complexity and privacy protection performance of these solutions.Finally,we discuss open issues to facilitate further studies.
文摘Recently,the application of Wireless Sensor Networks(WSNs)has been increasing rapidly.It requires privacy preserving data aggregation protocols to secure the data from compromises.Preserving privacy of the sensor data is a challenging task.This paper presents a non-linear regression-based data aggregation protocol for preserving privacy of the sensor data.The proposed protocol uses non-linear regression functions to represent the sensor data collected from the sensor nodes.Instead of sending the complete data to the cluster head,the sensor nodes only send the coefficients of the non-linear function.This will reduce the communication overhead of the network.The data aggregation is performed on the masked coefficients and the sink node is able to retrieve the approximated results over the aggregated data.The analysis of experiment results shows that the proposed protocol is able to minimize communication overhead,enhance data aggregation accuracy,and preserve data privacy.
基金supported partly by the following funding agencies:the National Natural Science Foundation(No.61170274)the Innovative Research Groups of the National Natural Science Foundation(No.61121061)+1 种基金the National Key Basic Research Program of China (No.2011CB302506)Youth Scientific Research and Innovation Plan of Beijing University of Posts and Telecommunications(No. 2013RC1101)
文摘In this paper,we study the problem of privacy-preserving top-k keyword similarity search over outsourced cloud data.Taking edit distance as a measure of similarity,we first build up the similarity keyword sets for all the keywords in the data collection.We then calculate the relevance scores of the elements in the similarity keyword sets by the widely used tf-idf theory.Leveraging both the similarity keyword sets and the relevance scores,we present a new secure and efficient treebased index structure for privacy-preserving top-k keyword similarity search.To prevent potential statistical attacks,we also introduce a two-server model to separate the association between the index structure and the data collection in cloud servers.Thorough analysis is given on the validity of search functionality and formal security proofs are presented for the privacy guarantee of our solution.Experimental results on real-world data sets further demonstrate the availability and efficiency of our solution.
基金supported in part by the National Key Research and Development Program of China 2020YFB2104500.
文摘With the increasing development of smart grid,multi-party cooperative computation between several entities has become a typical characteristic of modern energy systems.Traditionally,data exchange among parties is inevitable,rendering how to complete multi-party collaborative optimization without exposing any private information a critical issue.This paper proposes a fully privacy-preserving distributed optimization framework based on secure multi-party computation(SMPC)with secret sharing protocols.The framework decomposes the collaborative optimization problem into a master problem and several subproblems.The process of solving the master problem is executed in the SMPC framework via the secret sharing protocols among agents.The relationships of agents are completely equal,and there is no privileged agent or any third party.The process of solving subproblems is conducted by agents individually.Compared to the traditional distributed optimization framework,the proposed SMPC-based framework can fully preserve individual private information.Exchanged data among agents are encrypted and no private information disclosure is assured.Furthermore,the framework maintains a limited and acceptable increase in computational costs while guaranteeing opti-mality.Case studies are conducted on test systems of different scales to demonstrate the principle of secret sharing and verify the feasibility and scalability of the proposed methodology.
文摘This paper presents a novel privacy principle, ε-inclusion, for re-publishing sensitive dynamic datasets. ε-inclusion releases all the quasi-identifier values directly and uses permutation-based method and substitution to anonymize the microdata. Combined with generalization-based methods, ε-inclusion protects privacy and captures a large amount of correlation in the microdata. We develop an effective algorithm for computing anonymized tables that obey the ε-inclusion privacy requirement. Extensive experiments confirm that our solution allows significantly more effective data analysis than generalization-based methods.
