The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facili...The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facilitating fine-grained access control,Ciphertext Policy Attribute-Based Encryption(CP-ABE)can effectively ensure the confidentiality of shared data.Nevertheless,the conventional centralized CP-ABE scheme is plagued by the issues of keymisuse,key escrow,and large computation,which will result in security risks.This paper suggests a lightweight IoT data security sharing scheme that integrates blockchain technology and CP-ABE to address the abovementioned issues.The integrity and traceability of shared data are guaranteed by the use of blockchain technology to store and verify access transactions.The encryption and decryption operations of the CP-ABE algorithm have been implemented using elliptic curve scalarmultiplication to accommodate lightweight IoT devices,as opposed to themore arithmetic bilinear pairing found in the traditional CP-ABE algorithm.Additionally,a portion of the computation is delegated to the edge nodes to alleviate the computational burden on users.A distributed key management method is proposed to address the issues of key escrow andmisuse.Thismethod employs the edge blockchain to facilitate the storage and distribution of attribute private keys.Meanwhile,data security sharing is enhanced by combining off-chain and on-chain ciphertext storage.The security and performance analysis indicates that the proposed scheme is more efficient and secure.展开更多
With the rapid development of web3.0 applications,the volume of data sharing is increasing,the inefficiency of big data file sharing and the problem of data privacy leakage are becoming more and more prominent,and the...With the rapid development of web3.0 applications,the volume of data sharing is increasing,the inefficiency of big data file sharing and the problem of data privacy leakage are becoming more and more prominent,and the existing data sharing schemes have been difficult to meet the growing demand for data sharing,this paper aims at exploring a secure,efficient and privacy-protecting data sharing scheme under web3.0 applications.Specifically,this paper adopts interplanetary file system(IPFS)technology to realize the storage of large data files to solve the problem of blockchain storage capacity limitation,and utilizes ciphertext policy attribute-based encryption(CP-ABE)and proxy re-encryption(PRE)technology to realize secure multi-party sharing and finegrained access control of data.This paper provides the detailed algorithm design and implementation of data sharing phases and processes,and analyzes the algorithms from the perspectives of security,privacy protection,and performance.展开更多
With the rapid development of medical data sharing,issues of privacy and ownership have become prominent,which have limited the scale of data sharing.To address the above challenges,we propose a blockchainbased data-s...With the rapid development of medical data sharing,issues of privacy and ownership have become prominent,which have limited the scale of data sharing.To address the above challenges,we propose a blockchainbased data-sharing framework to ensure data security and encourage data owners to actively participate in sharing.We introduce a reliable attribute-based searchable encryption scheme that enables fine-grained access control of encrypted data and ensures secure and efficient data sharing.The revenue distribution model is constructed based on Shapley value to motivate participants.Additionally,by integrating the smart contract technology of blockchain,the search operation and incentive mechanism are automatically executed.Through revenue distribution analysis,the incentive effect and rationality of the proposed scheme are verified.Performance evaluation shows that,compared with traditional data-sharing models,our proposed framework not only meets data security requirements but also incentivizes more participants to actively participate in data sharing.展开更多
The advent of the digital age has consistently provided impetus for facilitating global trade,as evidenced by the numerous customs clearance documents and participants involved in the international trade process,inclu...The advent of the digital age has consistently provided impetus for facilitating global trade,as evidenced by the numerous customs clearance documents and participants involved in the international trade process,including enterprises,agents,and government departments.However,the urgent issue that requires immediate attention is how to achieve secure and efficient cross-border data sharing among these government departments and enterprises in complex trade processes.In addressing this need,this paper proposes a data exchange architecture employing Multi-Authority Attribute-Based Encryption(MA-ABE)in combination with blockchain technology.This scheme supports proxy decryption,attribute revocation,and policy update,while allowing each participating entity to manage their keys autonomously,ensuring system security and enhancing trust among participants.In order to enhance system decentralization,a mechanism has been designed in the architecture where multiple institutions interact with smart contracts and jointly participate in the generation of public parameters.Integration with the multi-party process execution engine Caterpillar has been shown to boost the transparency of cross-border information flow and cooperation between different organizations.The scheme ensures the auditability of data access control information and the visualization of on-chain data sharing.The MA-ABE scheme is statically secure under the q-Decisional Parallel Bilinear Diffie-Hellman Exponent(q-DPBDHE2)assumption in the random oracle model,and can resist ciphertext rollback attacks to achieve true backward and forward security.Theoretical analysis and experimental results demonstrate the appropriateness of the scheme for cross-border data collaboration between different institutions.展开更多
For the goals of security and privacy preservation,we propose a blind batch encryption-and public ledger-based data sharing protocol that allows the integrity of sensitive data to be audited by a public ledger and all...For the goals of security and privacy preservation,we propose a blind batch encryption-and public ledger-based data sharing protocol that allows the integrity of sensitive data to be audited by a public ledger and allows privacy information to be preserved.Data owners can tightly manage their data with efficient revocation and only grant one-time adaptive access for the fulfillment of the requester.We prove that our protocol is semanticallly secure,blind,and secure against oblivious requesters and malicious file keepers.We also provide security analysis in the context of four typical attacks.展开更多
Traditional Io T systems suffer from high equipment management costs and difficulty in trustworthy data sharing caused by centralization.Blockchain provides a feasible research direction to solve these problems. The m...Traditional Io T systems suffer from high equipment management costs and difficulty in trustworthy data sharing caused by centralization.Blockchain provides a feasible research direction to solve these problems. The main challenge at this stage is to integrate the blockchain from the resourceconstrained Io T devices and ensure the data of Io T system is credible. We provide a general framework for intelligent Io T data acquisition and sharing in an untrusted environment based on the blockchain, where gateways become Oracles. A distributed Oracle network based on Byzantine Fault Tolerant algorithm is used to provide trusted data for the blockchain to make intelligent Io T data trustworthy. An aggregation contract is deployed to collect data from various Oracle and share the credible data to all on-chain users. We also propose a gateway data aggregation scheme based on the REST API event publishing/subscribing mechanism which uses SQL to achieve flexible data aggregation. The experimental results show that the proposed scheme can alleviate the problem of limited performance of Io T equipment, make data reliable, and meet the diverse data needs on the chain.展开更多
With the development of technology,the connected vehicle has been upgraded from a traditional transport vehicle to an information terminal and energy storage terminal.The data of ICV(intelligent connected vehicles)is ...With the development of technology,the connected vehicle has been upgraded from a traditional transport vehicle to an information terminal and energy storage terminal.The data of ICV(intelligent connected vehicles)is the key to organically maximizing their efficiency.However,in the context of increasingly strict global data security supervision and compliance,numerous problems,including complex types of connected vehicle data,poor data collaboration between the IT(information technology)domain and OT(operation technology)domain,different data format standards,lack of shared trust sources,difficulty in ensuring the quality of shared data,lack of data control rights,as well as difficulty in defining data ownership,make vehicle data sharing face a lot of problems,and data islands are widespread.This study proposes FADSF(Fuzzy Anonymous Data Share Frame),an automobile data sharing scheme based on blockchain.The data holder publishes the shared data information and forms the corresponding label storage on the blockchain.The data demander browses the data directory information to select and purchase data assets and verify them.The data demander selects and purchases data assets and verifies them by browsing the data directory information.Meanwhile,this paper designs a data structure Data Discrimination Bloom Filter(DDBF),making complaints about illegal data.When the number of data complaints reaches the threshold,the audit traceability contract is triggered to punish the illegal data publisher,aiming to improve the data quality and maintain a good data sharing ecology.In this paper,based on Ethereum,the above scheme is tested to demonstrate its feasibility,efficiency and security.展开更多
Sharing data while protecting privacy in the industrial Internet is a significant challenge.Traditional machine learning methods require a combination of all data for training;however,this approach can be limited by d...Sharing data while protecting privacy in the industrial Internet is a significant challenge.Traditional machine learning methods require a combination of all data for training;however,this approach can be limited by data availability and privacy concerns.Federated learning(FL)has gained considerable attention because it allows for decentralized training on multiple local datasets.However,the training data collected by data providers are often non-independent and identically distributed(non-IID),resulting in poor FL performance.This paper proposes a privacy-preserving approach for sharing non-IID data in the industrial Internet using an FL approach based on blockchain technology.To overcome the problem of non-IID data leading to poor training accuracy,we propose dynamically updating the local model based on the divergence of the global and local models.This approach can significantly improve the accuracy of FL training when there is relatively large dispersion.In addition,we design a dynamic gradient clipping algorithm to alleviate the influence of noise on the model accuracy to reduce potential privacy leakage caused by sharing model parameters.Finally,we evaluate the performance of the proposed scheme using commonly used open-source image datasets.The simulation results demonstrate that our method can significantly enhance the accuracy while protecting privacy and maintaining efficiency,thereby providing a new solution to data-sharing and privacy-protection challenges in the industrial Internet.展开更多
In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning method.We demonstrated that the training dataset has a significant impact on the trainin...In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning method.We demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model structure.However,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial obstacle.This issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data security.To address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.展开更多
In the BESⅢdetector at Beijing electron-positron collider,billions of events from e^(+)e^(-)collisions were recorded.These events passing through the trigger system were saved in raw data format files.They play an im...In the BESⅢdetector at Beijing electron-positron collider,billions of events from e^(+)e^(-)collisions were recorded.These events passing through the trigger system were saved in raw data format files.They play an important role in the study of physics inτ-charm energy region.Here,we published an e^(+)e^(-)collision dataset containing both Monte Carlo simulation samples and real data collected by the BESⅢdetector.The data pass through the detector trigger system,file format conversion,and physics information extraction and finally save the physics information and detector response in text format files.This dataset is publicly available and is intended to provide interested scientists and those outside of the BESⅢcollaboration with event information from BESⅢ,which can be used to understand physics research in e^(+)e^(-)collisions,developing visualization projects for physics education,public outreach,and science advocacy.展开更多
To reconstruct vehicle accidents,data from the time of the incident—such as pre-collision speed and collision point—is essential.This data is collected and generated through various sensors installed in the vehicle....To reconstruct vehicle accidents,data from the time of the incident—such as pre-collision speed and collision point—is essential.This data is collected and generated through various sensors installed in the vehicle.However,it may contain sensitive information about the vehicle owner.Consequently,vehicle owners tend to be reluctant to provide their vehicle data due to concerns about personal information exposure.Therefore,extensive research has been conducted on secure vehicle data trading models.Existing models primarily utilize centralized approaches,leading to issues such as single points of failure,data leakage,and manipulation.To address these problems,this paper proposes ORTHRUS,a blockchain-based vehicle data trading marketplace that ensures transparency,traceability,and decentralization.The proposed model accommodates two categories of output data:the original data and the computed result from the function.Additionally,in the proposed model,data owners retain control over their data,enabling them to directly choose which types of data to provide.By employing Multi-party computation(MPC)technique,MOZAIK architecture,and the random leader selection technique,the proposed scheme,ORTHRUS,guarantees the input privacy and resistance to pre-collusion attacks.Furthermore,the proposed model promotes fairness by identifying dishonest behavior among participants by enforcing penalties and rewards through the implementation of smart contracts.展开更多
【Objective】Medical imaging data has great value,but it contains a significant amount of sensitive information about patients.At present,laws and regulations regarding to the de-identification of medical imaging data...【Objective】Medical imaging data has great value,but it contains a significant amount of sensitive information about patients.At present,laws and regulations regarding to the de-identification of medical imaging data are not clearly defined around the world.This study aims to develop a tool that meets compliance-driven desensitization requirements tailored to diverse research needs.【Methods】To enhance the security of medical image data,we designed and implemented a DICOM format medical image de-identification system on the Windows operating system.【Results】Our custom de-identification system is adaptable to the legal standards of different countries and can accommodate specific research demands.The system offers both web-based online and desktop offline de-identification capabilities,enabling customization of de-identification rules and facilitating batch processing to improve efficiency.【Conclusions】This medical image de-identification system robustly strengthens the stewardship of sensitive medical data,aligning with data security protection requirements while facilitating the sharing and utilization of medical image data.This approach unlocks the intrinsic value inherent in such datasets.展开更多
As the information sensing and processing capabilities of IoT devices increase,a large amount of data is being generated at the edge of Industrial IoT(IIoT),which has become a strong foundation for distributed Artific...As the information sensing and processing capabilities of IoT devices increase,a large amount of data is being generated at the edge of Industrial IoT(IIoT),which has become a strong foundation for distributed Artificial Intelligence(AI)applications.However,most users are reluctant to disclose their data due to network bandwidth limitations,device energy consumption,and privacy requirements.To address this issue,this paper introduces an Edge-assisted Federated Learning(EFL)framework,along with an incentive mechanism for lightweight industrial data sharing.In order to reduce the information asymmetry between data owners and users,an EFL model-sharing incentive mechanism based on contract theory is designed.In addition,a weight dispersion evaluation scheme based on Wasserstein distance is proposed.This study models an optimization problem of node selection and sharing incentives to maximize the EFL model consumers'profit and ensure the quality of training services.An incentive-based EFL algorithm with individual rationality and incentive compatibility constraints is proposed.Finally,the experimental results verify the effectiveness of the proposed scheme in terms of positive incentives for contract design and performance analysis of EFL systems.展开更多
Photonuclear data are increasingly used in fundamental nuclear research and technological applications.These data are generated using advanced γ-ray sources.The Shanghai laser electron gamma source(SLEGS)is a new las...Photonuclear data are increasingly used in fundamental nuclear research and technological applications.These data are generated using advanced γ-ray sources.The Shanghai laser electron gamma source(SLEGS)is a new laser Compton scattering γ-ray source at the Shanghai Synchrotron Radiation Facility.It delivers energy-tunable,quasi-monoenergetic gamma beams for high-precision photonuclear measurements.This paper presents the flat-efficiency detector(FED)array at SLEGS and its application in photoneutron cross-section measurements.Systematic uncertainties of the FED array were determined to be 3.02%through calibration with a ^(252)Cf neutron source.Using ^(197)Au and ^(159)Tb as representative nuclei,we demonstrate the format and processing methodology for raw photoneutron data.The results validate SLEGS’capability for high-precision photoneutron measurements.展开更多
Data space,as an innovative data management and sharing model,is emerging in the medical and health sectors.This study expounds on the conceptual connotation of data space and delineates its key technologies,including...Data space,as an innovative data management and sharing model,is emerging in the medical and health sectors.This study expounds on the conceptual connotation of data space and delineates its key technologies,including distributed data storage,standardization and interoperability of data sharing,data security and privacy protection,data analysis and mining,and data space assessment.By analyzing the real-world cases of data spaces within medicine and health,this study compares the similarities and differences across various dimensions such as purpose,architecture,data interoperability,and privacy protection.Meanwhile,data spaces in these fields are challenged by the limited computing resources,the complexities of data integration,and the need for optimized algorithms.Additionally,legal and ethical issues such as unclear data ownership,undefined usage rights,risks associated with privacy protection need to be addressed.The study notes organizational and management difficulties,calling for enhancements in governance framework,data sharing mechanisms,and value assessment systems.In the future,technological innovation,sound regulations,and optimized management will help the development of the medical and health data space.These developments will enable the secure and efficient utilization of data,propelling the medical industry into an era characterized by precision,intelligence,and personalization.展开更多
This article introduces the methodologies and instrumentation for data measurement and propagation at the Back-n white neutron facility of the China Spallation Neutron Source.The Back-n facility employs backscattering...This article introduces the methodologies and instrumentation for data measurement and propagation at the Back-n white neutron facility of the China Spallation Neutron Source.The Back-n facility employs backscattering techniques to generate a broad spectrum of white neutrons.Equipped with advanced detectors such as the light particle detector array and the fission ionization chamber detector,the facility achieves high-precision data acquisition through a general-purpose electronics system.Data were managed and stored in a hierarchical system supported by the National High Energy Physics Science Data Center,ensuring long-term preservation and efficient access.The data from the Back-n experiments significantly contribute to nuclear physics,reactor design,astrophysics,and medical physics,enhancing the understanding of nuclear processes and supporting interdisciplinary research.展开更多
As the integration of medical big data and artificial intelligence advances,the secure sharing of medical data has become a key driving force for advancing disease research and clinical diagnosis.Federated learning,a ...As the integration of medical big data and artificial intelligence advances,the secure sharing of medical data has become a key driving force for advancing disease research and clinical diagnosis.Federated learning,a distributed approach enabling collaborative data processing without sharing raw data,offers promising solutions to challenges in multi-center medical data sharing.This review summarizes the progress of federated learning in multi-center medical data processing,analyzed from four perspectives:system architectures,data distribution strategies,clinical tasks,and algorithmic models.At the same time,this paper explores the challenges in practical applications,such as data heterogeneity,communication overhead,and privacy concerns.It proposes driving future research development by optimizing algorithms,strengthening privacy protection mechanisms,and enhancing computational efficiency.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
In the digital era,electronic medical record(EMR)has been a major way for hospitals to store patients’medical data.The traditional centralized medical system and semi-trusted cloud storage are difficult to achieve dy...In the digital era,electronic medical record(EMR)has been a major way for hospitals to store patients’medical data.The traditional centralized medical system and semi-trusted cloud storage are difficult to achieve dynamic balance between privacy protection and data sharing.The storage capacity of blockchain is limited and single blockchain schemes have poor scalability and low throughput.To address these issues,we propose a secure and efficient medical data storage and sharing scheme based on double blockchain.In our scheme,we encrypt the original EMR and store it in the cloud.The storage blockchain stores the index of the complete EMR,and the shared blockchain stores the index of the shared part of the EMR.Users with different attributes can make requests to different blockchains to share different parts according to their own permissions.Through experiments,it was found that cloud storage combined with blockchain not only solved the problem of limited storage capacity of blockchain,but also greatly reduced the risk of leakage of the original EMR.Content Extraction Signature(CES)combined with the double blockchain technology realized the separation of the privacy part and the shared part of the original EMR.The symmetric encryption technology combined with Ciphertext-Policy Attribute-Based Encryption(CP–ABE)not only ensures the safe storage of data in the cloud,but also achieves the consistency and convenience of data update,avoiding redundant backup of data.Safety analysis and performance analysis verified the feasibility and effectiveness of our scheme.展开更多
With the development of the Internet of Things(IoT),the massive data sharing between IoT devices improves the Quality of Service(QoS)and user experience in various IoT applications.However,data sharing may cause serio...With the development of the Internet of Things(IoT),the massive data sharing between IoT devices improves the Quality of Service(QoS)and user experience in various IoT applications.However,data sharing may cause serious privacy leakages to data providers.To address this problem,in this study,data sharing is realized through model sharing,based on which a secure data sharing mechanism,called BP2P-FL,is proposed using peer-to-peer federated learning with the privacy protection of data providers.In addition,by introducing the blockchain to the data sharing,every training process is recorded to ensure that data providers offer high-quality data.For further privacy protection,the differential privacy technology is used to disturb the global data sharing model.The experimental results show that BP2P-FL has high accuracy and feasibility in the data sharing of various IoT applications.展开更多
文摘The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facilitating fine-grained access control,Ciphertext Policy Attribute-Based Encryption(CP-ABE)can effectively ensure the confidentiality of shared data.Nevertheless,the conventional centralized CP-ABE scheme is plagued by the issues of keymisuse,key escrow,and large computation,which will result in security risks.This paper suggests a lightweight IoT data security sharing scheme that integrates blockchain technology and CP-ABE to address the abovementioned issues.The integrity and traceability of shared data are guaranteed by the use of blockchain technology to store and verify access transactions.The encryption and decryption operations of the CP-ABE algorithm have been implemented using elliptic curve scalarmultiplication to accommodate lightweight IoT devices,as opposed to themore arithmetic bilinear pairing found in the traditional CP-ABE algorithm.Additionally,a portion of the computation is delegated to the edge nodes to alleviate the computational burden on users.A distributed key management method is proposed to address the issues of key escrow andmisuse.Thismethod employs the edge blockchain to facilitate the storage and distribution of attribute private keys.Meanwhile,data security sharing is enhanced by combining off-chain and on-chain ciphertext storage.The security and performance analysis indicates that the proposed scheme is more efficient and secure.
基金supported by the National Natural Science Foundation of China(Grant No.U24B20146)the National Key Research and Development Plan in China(Grant No.2020YFB1005500)Beijing Natural Science Foundation Project(No.M21034).
文摘With the rapid development of web3.0 applications,the volume of data sharing is increasing,the inefficiency of big data file sharing and the problem of data privacy leakage are becoming more and more prominent,and the existing data sharing schemes have been difficult to meet the growing demand for data sharing,this paper aims at exploring a secure,efficient and privacy-protecting data sharing scheme under web3.0 applications.Specifically,this paper adopts interplanetary file system(IPFS)technology to realize the storage of large data files to solve the problem of blockchain storage capacity limitation,and utilizes ciphertext policy attribute-based encryption(CP-ABE)and proxy re-encryption(PRE)technology to realize secure multi-party sharing and finegrained access control of data.This paper provides the detailed algorithm design and implementation of data sharing phases and processes,and analyzes the algorithms from the perspectives of security,privacy protection,and performance.
基金supported by the Natural Science Foundation of Hebei Province of China(F2021201052).
文摘With the rapid development of medical data sharing,issues of privacy and ownership have become prominent,which have limited the scale of data sharing.To address the above challenges,we propose a blockchainbased data-sharing framework to ensure data security and encourage data owners to actively participate in sharing.We introduce a reliable attribute-based searchable encryption scheme that enables fine-grained access control of encrypted data and ensures secure and efficient data sharing.The revenue distribution model is constructed based on Shapley value to motivate participants.Additionally,by integrating the smart contract technology of blockchain,the search operation and incentive mechanism are automatically executed.Through revenue distribution analysis,the incentive effect and rationality of the proposed scheme are verified.Performance evaluation shows that,compared with traditional data-sharing models,our proposed framework not only meets data security requirements but also incentivizes more participants to actively participate in data sharing.
基金supported by Hainan Provincial Natural Science Foundation of China Nos.622RC617,624RC485Open Foundation of State Key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications)(SKLNST-2023-1-07).
文摘The advent of the digital age has consistently provided impetus for facilitating global trade,as evidenced by the numerous customs clearance documents and participants involved in the international trade process,including enterprises,agents,and government departments.However,the urgent issue that requires immediate attention is how to achieve secure and efficient cross-border data sharing among these government departments and enterprises in complex trade processes.In addressing this need,this paper proposes a data exchange architecture employing Multi-Authority Attribute-Based Encryption(MA-ABE)in combination with blockchain technology.This scheme supports proxy decryption,attribute revocation,and policy update,while allowing each participating entity to manage their keys autonomously,ensuring system security and enhancing trust among participants.In order to enhance system decentralization,a mechanism has been designed in the architecture where multiple institutions interact with smart contracts and jointly participate in the generation of public parameters.Integration with the multi-party process execution engine Caterpillar has been shown to boost the transparency of cross-border information flow and cooperation between different organizations.The scheme ensures the auditability of data access control information and the visualization of on-chain data sharing.The MA-ABE scheme is statically secure under the q-Decisional Parallel Bilinear Diffie-Hellman Exponent(q-DPBDHE2)assumption in the random oracle model,and can resist ciphertext rollback attacks to achieve true backward and forward security.Theoretical analysis and experimental results demonstrate the appropriateness of the scheme for cross-border data collaboration between different institutions.
基金partially supported by the National Natural Science Foundation of China under grant no.62372245the Foundation of Yunnan Key Laboratory of Blockchain Application Technology under Grant 202105AG070005+1 种基金in part by the Foundation of State Key Laboratory of Public Big Datain part by the Foundation of Key Laboratory of Computational Science and Application of Hainan Province under Grant JSKX202202。
文摘For the goals of security and privacy preservation,we propose a blind batch encryption-and public ledger-based data sharing protocol that allows the integrity of sensitive data to be audited by a public ledger and allows privacy information to be preserved.Data owners can tightly manage their data with efficient revocation and only grant one-time adaptive access for the fulfillment of the requester.We prove that our protocol is semanticallly secure,blind,and secure against oblivious requesters and malicious file keepers.We also provide security analysis in the context of four typical attacks.
基金supported by the open research fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology(Nanjing University of Posts and Telecommunications),Ministry of Education(No.JZNY202114)Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX210734).
文摘Traditional Io T systems suffer from high equipment management costs and difficulty in trustworthy data sharing caused by centralization.Blockchain provides a feasible research direction to solve these problems. The main challenge at this stage is to integrate the blockchain from the resourceconstrained Io T devices and ensure the data of Io T system is credible. We provide a general framework for intelligent Io T data acquisition and sharing in an untrusted environment based on the blockchain, where gateways become Oracles. A distributed Oracle network based on Byzantine Fault Tolerant algorithm is used to provide trusted data for the blockchain to make intelligent Io T data trustworthy. An aggregation contract is deployed to collect data from various Oracle and share the credible data to all on-chain users. We also propose a gateway data aggregation scheme based on the REST API event publishing/subscribing mechanism which uses SQL to achieve flexible data aggregation. The experimental results show that the proposed scheme can alleviate the problem of limited performance of Io T equipment, make data reliable, and meet the diverse data needs on the chain.
基金This work was financially supported by the National Key Research and Development Program of China(2022YFB3103200).
文摘With the development of technology,the connected vehicle has been upgraded from a traditional transport vehicle to an information terminal and energy storage terminal.The data of ICV(intelligent connected vehicles)is the key to organically maximizing their efficiency.However,in the context of increasingly strict global data security supervision and compliance,numerous problems,including complex types of connected vehicle data,poor data collaboration between the IT(information technology)domain and OT(operation technology)domain,different data format standards,lack of shared trust sources,difficulty in ensuring the quality of shared data,lack of data control rights,as well as difficulty in defining data ownership,make vehicle data sharing face a lot of problems,and data islands are widespread.This study proposes FADSF(Fuzzy Anonymous Data Share Frame),an automobile data sharing scheme based on blockchain.The data holder publishes the shared data information and forms the corresponding label storage on the blockchain.The data demander browses the data directory information to select and purchase data assets and verify them.The data demander selects and purchases data assets and verifies them by browsing the data directory information.Meanwhile,this paper designs a data structure Data Discrimination Bloom Filter(DDBF),making complaints about illegal data.When the number of data complaints reaches the threshold,the audit traceability contract is triggered to punish the illegal data publisher,aiming to improve the data quality and maintain a good data sharing ecology.In this paper,based on Ethereum,the above scheme is tested to demonstrate its feasibility,efficiency and security.
基金This work was supported by the National Key R&D Program of China under Grant 2023YFB2703802the Hunan Province Innovation and Entrepreneurship Training Program for College Students S202311528073.
文摘Sharing data while protecting privacy in the industrial Internet is a significant challenge.Traditional machine learning methods require a combination of all data for training;however,this approach can be limited by data availability and privacy concerns.Federated learning(FL)has gained considerable attention because it allows for decentralized training on multiple local datasets.However,the training data collected by data providers are often non-independent and identically distributed(non-IID),resulting in poor FL performance.This paper proposes a privacy-preserving approach for sharing non-IID data in the industrial Internet using an FL approach based on blockchain technology.To overcome the problem of non-IID data leading to poor training accuracy,we propose dynamically updating the local model based on the divergence of the global and local models.This approach can significantly improve the accuracy of FL training when there is relatively large dispersion.In addition,we design a dynamic gradient clipping algorithm to alleviate the influence of noise on the model accuracy to reduce potential privacy leakage caused by sharing model parameters.Finally,we evaluate the performance of the proposed scheme using commonly used open-source image datasets.The simulation results demonstrate that our method can significantly enhance the accuracy while protecting privacy and maintaining efficiency,thereby providing a new solution to data-sharing and privacy-protection challenges in the industrial Internet.
基金National Key Research and Development Program of China(2021ZD0113704).
文摘In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning method.We demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model structure.However,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial obstacle.This issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data security.To address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.
基金supported by the National Key Research and Development Program of China(No.2023YFA1606000)National Natural Science Foundation of China(Nos.12175321,11975021,and U1932101)National College Students Science and Technology Innovation Project of Sun Yat-sen University。
文摘In the BESⅢdetector at Beijing electron-positron collider,billions of events from e^(+)e^(-)collisions were recorded.These events passing through the trigger system were saved in raw data format files.They play an important role in the study of physics inτ-charm energy region.Here,we published an e^(+)e^(-)collision dataset containing both Monte Carlo simulation samples and real data collected by the BESⅢdetector.The data pass through the detector trigger system,file format conversion,and physics information extraction and finally save the physics information and detector response in text format files.This dataset is publicly available and is intended to provide interested scientists and those outside of the BESⅢcollaboration with event information from BESⅢ,which can be used to understand physics research in e^(+)e^(-)collisions,developing visualization projects for physics education,public outreach,and science advocacy.
基金supported by the IITP(Institute of Information&communications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2020-II201797)was supported as a‘Technology Commercialization Collaboration Platform Construction’project of the INNOPOLIS FOUNDATION(Project Number:1711202494).
文摘To reconstruct vehicle accidents,data from the time of the incident—such as pre-collision speed and collision point—is essential.This data is collected and generated through various sensors installed in the vehicle.However,it may contain sensitive information about the vehicle owner.Consequently,vehicle owners tend to be reluctant to provide their vehicle data due to concerns about personal information exposure.Therefore,extensive research has been conducted on secure vehicle data trading models.Existing models primarily utilize centralized approaches,leading to issues such as single points of failure,data leakage,and manipulation.To address these problems,this paper proposes ORTHRUS,a blockchain-based vehicle data trading marketplace that ensures transparency,traceability,and decentralization.The proposed model accommodates two categories of output data:the original data and the computed result from the function.Additionally,in the proposed model,data owners retain control over their data,enabling them to directly choose which types of data to provide.By employing Multi-party computation(MPC)technique,MOZAIK architecture,and the random leader selection technique,the proposed scheme,ORTHRUS,guarantees the input privacy and resistance to pre-collusion attacks.Furthermore,the proposed model promotes fairness by identifying dishonest behavior among participants by enforcing penalties and rewards through the implementation of smart contracts.
基金CAMS Innovation Fund for Medical Sciences(CIFMS):“Construction of an Intelligent Management and Efficient Utilization Technology System for Big Data in Population Health Science.”(2021-I2M-1-057)Key Projects of the Innovation Fund of the National Clinical Research Center for Orthopedics and Sports Rehabilitation:“National Orthopedics and Sports Rehabilitation Real-World Research Platform System Construction”(23-NCRC-CXJJ-ZD4)。
文摘【Objective】Medical imaging data has great value,but it contains a significant amount of sensitive information about patients.At present,laws and regulations regarding to the de-identification of medical imaging data are not clearly defined around the world.This study aims to develop a tool that meets compliance-driven desensitization requirements tailored to diverse research needs.【Methods】To enhance the security of medical image data,we designed and implemented a DICOM format medical image de-identification system on the Windows operating system.【Results】Our custom de-identification system is adaptable to the legal standards of different countries and can accommodate specific research demands.The system offers both web-based online and desktop offline de-identification capabilities,enabling customization of de-identification rules and facilitating batch processing to improve efficiency.【Conclusions】This medical image de-identification system robustly strengthens the stewardship of sensitive medical data,aligning with data security protection requirements while facilitating the sharing and utilization of medical image data.This approach unlocks the intrinsic value inherent in such datasets.
基金supported by the National Natural Science Foundation of China (No.62071070)Major science and technology special project of Science and Technology Department of Yunnan Province (202002AB080001-8)BUPT innovation&entrepreneurship support program (2023-YC-T031)。
文摘As the information sensing and processing capabilities of IoT devices increase,a large amount of data is being generated at the edge of Industrial IoT(IIoT),which has become a strong foundation for distributed Artificial Intelligence(AI)applications.However,most users are reluctant to disclose their data due to network bandwidth limitations,device energy consumption,and privacy requirements.To address this issue,this paper introduces an Edge-assisted Federated Learning(EFL)framework,along with an incentive mechanism for lightweight industrial data sharing.In order to reduce the information asymmetry between data owners and users,an EFL model-sharing incentive mechanism based on contract theory is designed.In addition,a weight dispersion evaluation scheme based on Wasserstein distance is proposed.This study models an optimization problem of node selection and sharing incentives to maximize the EFL model consumers'profit and ensure the quality of training services.An incentive-based EFL algorithm with individual rationality and incentive compatibility constraints is proposed.Finally,the experimental results verify the effectiveness of the proposed scheme in terms of positive incentives for contract design and performance analysis of EFL systems.
基金supported by National Key Research and Development Program of China(Nos.2022YFA1602404 and 2023YFA1606901)the National Natural Science Foundation of China(Nos.12275338,12388102,and U2441221)the Key Laboratory of Nuclear Data Foundation(JCKY2022201C152).
文摘Photonuclear data are increasingly used in fundamental nuclear research and technological applications.These data are generated using advanced γ-ray sources.The Shanghai laser electron gamma source(SLEGS)is a new laser Compton scattering γ-ray source at the Shanghai Synchrotron Radiation Facility.It delivers energy-tunable,quasi-monoenergetic gamma beams for high-precision photonuclear measurements.This paper presents the flat-efficiency detector(FED)array at SLEGS and its application in photoneutron cross-section measurements.Systematic uncertainties of the FED array were determined to be 3.02%through calibration with a ^(252)Cf neutron source.Using ^(197)Au and ^(159)Tb as representative nuclei,we demonstrate the format and processing methodology for raw photoneutron data.The results validate SLEGS’capability for high-precision photoneutron measurements.
文摘Data space,as an innovative data management and sharing model,is emerging in the medical and health sectors.This study expounds on the conceptual connotation of data space and delineates its key technologies,including distributed data storage,standardization and interoperability of data sharing,data security and privacy protection,data analysis and mining,and data space assessment.By analyzing the real-world cases of data spaces within medicine and health,this study compares the similarities and differences across various dimensions such as purpose,architecture,data interoperability,and privacy protection.Meanwhile,data spaces in these fields are challenged by the limited computing resources,the complexities of data integration,and the need for optimized algorithms.Additionally,legal and ethical issues such as unclear data ownership,undefined usage rights,risks associated with privacy protection need to be addressed.The study notes organizational and management difficulties,calling for enhancements in governance framework,data sharing mechanisms,and value assessment systems.In the future,technological innovation,sound regulations,and optimized management will help the development of the medical and health data space.These developments will enable the secure and efficient utilization of data,propelling the medical industry into an era characterized by precision,intelligence,and personalization.
基金supported by the National Key Research and Development Plan(No.2023YFA1606602)。
文摘This article introduces the methodologies and instrumentation for data measurement and propagation at the Back-n white neutron facility of the China Spallation Neutron Source.The Back-n facility employs backscattering techniques to generate a broad spectrum of white neutrons.Equipped with advanced detectors such as the light particle detector array and the fission ionization chamber detector,the facility achieves high-precision data acquisition through a general-purpose electronics system.Data were managed and stored in a hierarchical system supported by the National High Energy Physics Science Data Center,ensuring long-term preservation and efficient access.The data from the Back-n experiments significantly contribute to nuclear physics,reactor design,astrophysics,and medical physics,enhancing the understanding of nuclear processes and supporting interdisciplinary research.
基金supported and funded by the National Natural Science Foundation of China(82101079)the Key R&D Program of Jiangsu Province(BE2023836)the National Key Research and Development Program of China(SQ2023YFC2400025).
文摘As the integration of medical big data and artificial intelligence advances,the secure sharing of medical data has become a key driving force for advancing disease research and clinical diagnosis.Federated learning,a distributed approach enabling collaborative data processing without sharing raw data,offers promising solutions to challenges in multi-center medical data sharing.This review summarizes the progress of federated learning in multi-center medical data processing,analyzed from four perspectives:system architectures,data distribution strategies,clinical tasks,and algorithmic models.At the same time,this paper explores the challenges in practical applications,such as data heterogeneity,communication overhead,and privacy concerns.It proposes driving future research development by optimizing algorithms,strengthening privacy protection mechanisms,and enhancing computational efficiency.
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the Natural Science Foundation of Heilongjiang Province of China under Grant No.LC2016024Natural Science Foundation of the Jiangsu Higher Education Institutions Grant No.17KJB520044Six Talent Peaks Project in Jiangsu Province No.XYDXX–108.
文摘In the digital era,electronic medical record(EMR)has been a major way for hospitals to store patients’medical data.The traditional centralized medical system and semi-trusted cloud storage are difficult to achieve dynamic balance between privacy protection and data sharing.The storage capacity of blockchain is limited and single blockchain schemes have poor scalability and low throughput.To address these issues,we propose a secure and efficient medical data storage and sharing scheme based on double blockchain.In our scheme,we encrypt the original EMR and store it in the cloud.The storage blockchain stores the index of the complete EMR,and the shared blockchain stores the index of the shared part of the EMR.Users with different attributes can make requests to different blockchains to share different parts according to their own permissions.Through experiments,it was found that cloud storage combined with blockchain not only solved the problem of limited storage capacity of blockchain,but also greatly reduced the risk of leakage of the original EMR.Content Extraction Signature(CES)combined with the double blockchain technology realized the separation of the privacy part and the shared part of the original EMR.The symmetric encryption technology combined with Ciphertext-Policy Attribute-Based Encryption(CP–ABE)not only ensures the safe storage of data in the cloud,but also achieves the consistency and convenience of data update,avoiding redundant backup of data.Safety analysis and performance analysis verified the feasibility and effectiveness of our scheme.
基金This work is supported by National Natural Science Foundation of China under Grant No.U1905211 and 61702103Natural Science Foundation of Fujian Province under Grant No.2020J01167 and 2020J01169.
文摘With the development of the Internet of Things(IoT),the massive data sharing between IoT devices improves the Quality of Service(QoS)and user experience in various IoT applications.However,data sharing may cause serious privacy leakages to data providers.To address this problem,in this study,data sharing is realized through model sharing,based on which a secure data sharing mechanism,called BP2P-FL,is proposed using peer-to-peer federated learning with the privacy protection of data providers.In addition,by introducing the blockchain to the data sharing,every training process is recorded to ensure that data providers offer high-quality data.For further privacy protection,the differential privacy technology is used to disturb the global data sharing model.The experimental results show that BP2P-FL has high accuracy and feasibility in the data sharing of various IoT applications.
