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
Purpose:Explore the factors affecting medical data sharing in clinical research scenarios from the user’s perspective,reveal the differences between different user groups,and deepen the understanding of medical data ...Purpose:Explore the factors affecting medical data sharing in clinical research scenarios from the user’s perspective,reveal the differences between different user groups,and deepen the understanding of medical data sharing mechanisms.Design/methodology/approach:By integrating the UTAUT model,trust theory and self-efficacy theory,introducing the concepts of data transparency and individual innovation,and combining internal and external motivators,we constructed a conceptual model of medical data users’sharing behavior in clinical research scenarios.We conducted empirical research by collecting 360 pieces of first-hand data from clinical researchers.Findings:Among the internal motivators,effort expectation had a higher impact on sharing intention than performance expectation,individual innovation and self-efficacy had a higher impact on sharing behavior than trust.Trust does not show a significant impact on sharing intention,but it has a significant positive influence on sharing behavior.Among the external motivators,community influence and data transparency both positively affect sharing intention.In addition,users with different working years,professional status,data level needs,and different sharing experiences showed significant differences in healthcare data sharing.Research limitations:Our sample of clinical researchers from China was used as empirical data.Further research is needed to examine the generality of the study findings.Practical implications:The findings enhance healthcare data stakeholders’understanding of healthcare data sharing in clinical research scenarios and provide theoretical and practical insights for relevant researchers.Originality/value:In this study,the UTAUT model,trust theory and self-efficacy theory were integrated and applied to clinical research scenarios for the first time,and the concepts of data transparency and individual innovation were introduced,and the CRS-USB conceptual model was constructed and validated to extend the UTAUT model.展开更多
The availability and quantity of remotely sensed and terrestrial geospatial data sets are on the rise.Historically,these data sets have been analyzed and quarried on 2D desktop computers;however,immersive technologies...The availability and quantity of remotely sensed and terrestrial geospatial data sets are on the rise.Historically,these data sets have been analyzed and quarried on 2D desktop computers;however,immersive technologies and specifically immersive virtual reality(iVR)allow for the integration,visualization,analysis,and exploration of these 3D geospatial data sets.iVR can deliver remote and large-scale geospatial data sets to the laboratory,providing embodied experiences of field sites across the earth and beyond.We describe a workflow for the ingestion of geospatial data sets and the development of an iVR workbench,and present the application of these for an experience of Iceland’s Thrihnukar volcano where we:(1)combined satellite imagery with terrain elevation data to create a basic reconstruction of the physical site;(2)used terrestrial LiDAR data to provide a geo-referenced point cloud model of the magmatic-volcanic system,as well as the LiDAR intensity values for the identification of rock types;and(3)used Structure-from-Motion(SfM)to construct a photorealistic point cloud of the inside volcano.The workbench provides tools for the direct manipulation of the georeferenced data sets,including scaling,rotation,and translation,and a suite of geometric measurement tools,including length,area,and volume.Future developments will be inspired by an ongoing user study that formally evaluates the workbench’s mature components in the context of fieldwork and analyses activities.展开更多
In current cloud computing system, large amounts of sensitive data are shared to other cloud users. To keep these data confidentiality, data owners should encrypt their data before outsourcing. We choose proxy reencry...In current cloud computing system, large amounts of sensitive data are shared to other cloud users. To keep these data confidentiality, data owners should encrypt their data before outsourcing. We choose proxy reencryption （PRE） as the cloud data encryption technique. In a PRE system, a semi-trusted proxy can transform a ciphertext under one public key into a ciphertext of the same message under another public key, but the proxy cannot gain any information about the message. In this paper, we propose a certificateless PRE （CL-PRE） scheme without pairings. The security of the proposed scheme can be proved to be equivalent to the computational Dire- Hellman （CDH） problem in the random oracle model. Compared with other existing CL-PRE schemes, our scheme requires less computation cost and is significantly more efficient. The new scheme does not need the public key certificates to guarantee validity of public keys and solves the key escrow problem in identity-based public key cryptography.展开更多
To address the private data management problems and realize privacy-preserving data sharing,a blockchain-based transaction system named Ecare featuring information transparency,fairness and scalability is proposed.The...To address the private data management problems and realize privacy-preserving data sharing,a blockchain-based transaction system named Ecare featuring information transparency,fairness and scalability is proposed.The proposed system formulates multiple private data access control strategies,and realizes data trading and sharing through on-chain transactions,which makes transaction records transparent and immutable.In our system,the private data are encrypted,and the role-based account model ensures that access to the data requires owner’s authorization.Moreover,a new consensus protocol named Proof of Transactions(PoT)proposed by ourselves has been used to improve consensus efficiency.The value of Ecare is not only that it aggregates telemedicine,data transactions,and other features,but also that it translates these actions into transaction events stored in the blockchain,making them transparent and immutable to all participants.The proposed system can be extended to more general big data privacy protection and data transaction scenarios.展开更多
With the rapid growth of Internet of Things(IoT)based models,and the lack amount of data makes cloud computing resources insufficient.Hence,edge computing-based techniques are becoming more popular in present research...With the rapid growth of Internet of Things(IoT)based models,and the lack amount of data makes cloud computing resources insufficient.Hence,edge computing-based techniques are becoming more popular in present research domains that makes data storage,and processing effective at the network edges.There are several advanced features like parallel processing and data perception are available in edge computing.Still,there are some challenges in providing privacy and data security over networks.To solve the security issues in Edge Computing,Hash-based Message Authentication Code(HMAC)algorithm is used to provide solutions for preserving data from various attacks that happens with the distributed network nature.This paper proposed a Trust Model for Secure Data Sharing(TM-SDS)with HMAC algorithm.Here,data security is ensured with local and global trust levels with the centralized processing of cloud and by conserving resources effectively.Further,the proposed model achieved 84.25%of packet delivery ratio which is better compared to existing models in the resulting phase.The data packets are securely transmitted between entities in the proposed model and results showed that proposed TM-SDS model outperforms the existing models in an efficient manner.展开更多
Big data has a strong demand for a network infrastructure with the capability to support data sharing and retrieval efficiently. Information-centric networking （ICN） is an emerging approach to satisfy this demand, w...Big data has a strong demand for a network infrastructure with the capability to support data sharing and retrieval efficiently. Information-centric networking （ICN） is an emerging approach to satisfy this demand, where big data is cached ubiquitously in the network and retrieved using data names. However, existing authentication and authorization schemes rely mostly on centralized servers to provide certification and mediation services for data retrieval. This causes considerable traffic overhead for the secure distributed sharing of data. To solve this problem, we employ identity-based cryptography （IBC） to propose a Distributed Authentication and Authorization Scheme （DAAS）, where an identity-based signature （IBS） is used to achieve distributed verifications of the identities of publishers and users. Moreover, Ciphertext-Policy Attribnte-based encryption （CP-ABE） is used to enable the distributed and fine-grained authorization. DAAS consists of three phases： initialization, secure data publication, and secure data retrieval, which seamlessly integrate authentication and authorization with the in- terest/data communication paradigm in ICN. In particular, we propose trustworthy registration and Network Operator and Authority Manifest （NOAM） dissemination to provide initial secure registration and enable efficient authentication for global data retrieval. Meanwhile, Attribute Manifest （AM） distribution coupled with automatic attribute update is proposed to reduce the cost of attribute retrieval. We examine the performance of the proposed DAAS, which shows that it can achieve a lower bandwidth cost than existing schemes.展开更多
This paper aims to find a practical way of quantitatively representing the privacy of network data. A method of quantifying the privacy of network data anonymization based on similarity distance and entropy in the sce...This paper aims to find a practical way of quantitatively representing the privacy of network data. A method of quantifying the privacy of network data anonymization based on similarity distance and entropy in the scenario involving multiparty network data sharing with Trusted Third Party (TTP) is proposed. Simulations are then conducted using network data from different sources, and show that the measurement indicators defined in this paper can adequately quantify the privacy of the network. In particular, it can indicate the effect of the auxiliary information of the adversary on privacy.展开更多
Science data are very important resources for innovative research in all scientific disciplines. The Ministry of Science and Technology （MOST） of China has launched a comprehensive platform program for supporting sc...Science data are very important resources for innovative research in all scientific disciplines. The Ministry of Science and Technology （MOST） of China has launched a comprehensive platform program for supporting scientific innovations and agricultural science database construction and sharing project is one of the activities under this program supported by MOST. This paper briefly described the achievements of the Agricultural Science Data Center Project.展开更多
With the advancements in the era of artificial intelligence,blockchain,cloud computing,and big data,there is a need for secure,decentralized medical record storage and retrieval systems.While cloud storage solves stor...With the advancements in the era of artificial intelligence,blockchain,cloud computing,and big data,there is a need for secure,decentralized medical record storage and retrieval systems.While cloud storage solves storage issues,it is challenging to realize secure sharing of records over the network.Medi-block record in the healthcare system has brought a new digitalization method for patients’medical records.This centralized technology provides a symmetrical process between the hospital and doctors when patients urgently need to go to a different or nearby hospital.It enables electronic medical records to be available with the correct authentication and restricts access to medical data retrieval.Medi-block record is the consumer-centered healthcare data system that brings reliable and transparent datasets for the medical record.This study presents an extensive review of proposed solutions aiming to protect the privacy and integrity of medical data by securing data sharing for Medi-block records.It also aims to propose a comprehensive investigation of the recent advances in different methods of securing data sharing,such as using Blockchain technology,Access Control,Privacy-Preserving,Proxy Re-Encryption,and Service-On-Chain approach.Finally,we highlight the open issues and identify the challenges regarding secure data sharing for Medi-block records in the healthcare systems.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
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
Data sharing technology in Internet of Vehicles(Io V)has attracted great research interest with the goal of realizing intelligent transportation and traffic management.Meanwhile,the main concerns have been raised abou...Data sharing technology in Internet of Vehicles(Io V)has attracted great research interest with the goal of realizing intelligent transportation and traffic management.Meanwhile,the main concerns have been raised about the security and privacy of vehicle data.The mobility and real-time characteristics of vehicle data make data sharing more difficult in Io V.The emergence of blockchain and federated learning brings new directions.In this paper,a data-sharing model that combines blockchain and federated learning is proposed to solve the security and privacy problems of data sharing in Io V.First,we use federated learning to share data instead of exposing actual data and propose an adaptive differential privacy scheme to further balance the privacy and availability of data.Then,we integrate the verification scheme into the consensus process,so that the consensus computation can filter out low-quality models.Experimental data shows that our data-sharing model can better balance the relationship between data availability and privacy,and also has enhanced security.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
The traditional centralized data sharing systems have potential risks such as single point of failures and excessive working load on the central node.As a distributed and collaborative alternative,approaches based upo...The traditional centralized data sharing systems have potential risks such as single point of failures and excessive working load on the central node.As a distributed and collaborative alternative,approaches based upon blockchain have been explored recently for Internet of Things(IoTs).However,the access from a legitimate user may be denied without the pre-defined policy and data update on the blockchain could be costly to the owners.In this paper,we first address these issues by incorporating the Accountable Subgroup Multi-Signature(ASM)algorithm into the Attribute-based Access Control(ABAC)method with Policy Smart Contract,to provide a finegrained and flexible solution.Next,we propose a policy-based Chameleon Hash algorithm that allows the data to be updated in a reliable and convenient way by the authorized users.Finally,we evaluate our work by comparing its performance with the benchmarks.The results demonstrate significant improvement on the effectiveness and efficiency.展开更多
In this paper,we propose a novel fuzzy matching data sharing scheme named FADS for cloudedge communications.FADS allows users to specify their access policies,and enables receivers to obtain the data transmitted by th...In this paper,we propose a novel fuzzy matching data sharing scheme named FADS for cloudedge communications.FADS allows users to specify their access policies,and enables receivers to obtain the data transmitted by the senders if and only if the two sides meet their defined certain policies simultaneously.Specifically,we first formalize the definition and security models of fuzzy matching data sharing in cloud-edge environments.Then,we construct a concrete instantiation by pairing-based cryptosystem and the privacy-preserving set intersection on attribute sets from both sides to construct a concurrent matching over the policies.If the matching succeeds,the data can be decrypted.Otherwise,nothing will be revealed.In addition,FADS allows users to dynamically specify the policy for each time,which is an urgent demand in practice.A thorough security analysis demonstrates that FADS is of provable security under indistinguishable chosen ciphertext attack(IND-CCA)in random oracle model against probabilistic polynomial-time(PPT)adversary,and the desirable security properties of privacy and authenticity are achieved.Extensive experiments provide evidence that FADS is with acceptable efficiency.展开更多
An M_(S)7.4 earthquake struck west China in Maduo county,Guoluo prefecture,Qinghai province on May 22,2021,at 2:04 Beijing time(18:04 UTC on May 21,2021),which broke the quiet period of Chinese mainland for 1382 days ...An M_(S)7.4 earthquake struck west China in Maduo county,Guoluo prefecture,Qinghai province on May 22,2021,at 2:04 Beijing time(18:04 UTC on May 21,2021),which broke the quiet period of Chinese mainland for 1382 days without earthquakes of magnitude 7 or higher.The analysis of the seismic data sequence would play an important role in the in-depth study of the Maduo earthquake and the Bayan Har block.The Institute of Geophysics,China Earthquake Administration(CEA),compiled observation data recorded through 57 broadband seismometers within 500 km of the earthquake epicenter and intended to share for further researches in earthquake science community.The shared dataset included waveforms of the event and its sequence with magnitudes of 3.0 or higher that occurred between May 22-31,2021 with a sampling rate of 100 sps along with the continuous waveforms of 20 Hz and 100 Hz.Additionally,the seismic instrument response files also were shared.The event and continuous waveform records could be downloaded by submitting a request through the web platform of the Earthquake Science Data Center of the Institute of Geophysics,CEA(www.esdc.ac.cn).展开更多
Data sharing is a main application of cloud computing. Some existing solutions are proposed to provide flexible access control for outsourced data in the cloud. However, few attentions have been paid to group-oriented...Data sharing is a main application of cloud computing. Some existing solutions are proposed to provide flexible access control for outsourced data in the cloud. However, few attentions have been paid to group-oriented data sharing when multiple data owners want to share their private data for cooperative purposes. In this paper, we put forward a new paradigm, referred to as secure, scalable and efficient multi-owner(SSEM) data sharing in clouds. The SSEM integrates identity-based encryption and asymmetric group key agreement to enable group-oriented access control for data owners in a many-to-many sharing pattern. Moreover, with SSEM, users can join in or leave from the group conveniently with the privacy of both group data and user data.We proposed the key-ciphertext homomorphism technique to construct an SSEM scheme with short ciphertexts. The security analysis shows that our SSEM scheme achieves data security against unauthorized accesses and collusion attacks. Both theoretical and experimental results confirm that our proposed scheme takes users little costs to share and access outsourced data in a group manner.展开更多
Due to the fact that consumers'privacy data sharing has multifaceted and complex effects on the e-commerce platform and its two sided agents,consumers and sellers,a game-theoretic model in a monopoly e-market is s...Due to the fact that consumers'privacy data sharing has multifaceted and complex effects on the e-commerce platform and its two sided agents,consumers and sellers,a game-theoretic model in a monopoly e-market is set up to study the equilibrium strategies of the three agents(the platform,the seller on it and consumers)under privacy data sharing.Equilibrium decisions show that after sharing consumers'privacy data once,the platform can collect more privacy data from consumers.Meanwhile,privacy data sharing pushes the seller to reduce the product price.Moreover,the platform will increase the transaction fee if the privacy data sharing value is high.It is also indicated that privacy data sharing always benefits consumers and the seller.However,the platform's profit decreases if the privacy data sharing value is low and the privacy data sharing level is high.Finally,an extended model considering an incomplete information game among the agents is discussed.The results show that both the platform and the seller cannot obtain a high profit from privacy data sharing.Factors including the seller's possibility to buy privacy data,the privacy data sharing value and privacy data sharing level affect the two agents'payoffs.If the platform wishes to benefit from privacy data sharing,it should increase the possibility of the seller to buy privacy data or increase the privacy data sharing value.展开更多
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 the National Natural Science Foundation of China(Grant No.72374081)the Key Research and Development Project of the Department of Science and Technology of Jilin Province(Grant No.20240304164SF).
文摘Purpose:Explore the factors affecting medical data sharing in clinical research scenarios from the user’s perspective,reveal the differences between different user groups,and deepen the understanding of medical data sharing mechanisms.Design/methodology/approach:By integrating the UTAUT model,trust theory and self-efficacy theory,introducing the concepts of data transparency and individual innovation,and combining internal and external motivators,we constructed a conceptual model of medical data users’sharing behavior in clinical research scenarios.We conducted empirical research by collecting 360 pieces of first-hand data from clinical researchers.Findings:Among the internal motivators,effort expectation had a higher impact on sharing intention than performance expectation,individual innovation and self-efficacy had a higher impact on sharing behavior than trust.Trust does not show a significant impact on sharing intention,but it has a significant positive influence on sharing behavior.Among the external motivators,community influence and data transparency both positively affect sharing intention.In addition,users with different working years,professional status,data level needs,and different sharing experiences showed significant differences in healthcare data sharing.Research limitations:Our sample of clinical researchers from China was used as empirical data.Further research is needed to examine the generality of the study findings.Practical implications:The findings enhance healthcare data stakeholders’understanding of healthcare data sharing in clinical research scenarios and provide theoretical and practical insights for relevant researchers.Originality/value:In this study,the UTAUT model,trust theory and self-efficacy theory were integrated and applied to clinical research scenarios for the first time,and the concepts of data transparency and individual innovation were introduced,and the CRS-USB conceptual model was constructed and validated to extend the UTAUT model.
基金This work was supported by the National Science Foundation[grant numbers 1526520 to AK and 0711456 to PL].
文摘The availability and quantity of remotely sensed and terrestrial geospatial data sets are on the rise.Historically,these data sets have been analyzed and quarried on 2D desktop computers;however,immersive technologies and specifically immersive virtual reality(iVR)allow for the integration,visualization,analysis,and exploration of these 3D geospatial data sets.iVR can deliver remote and large-scale geospatial data sets to the laboratory,providing embodied experiences of field sites across the earth and beyond.We describe a workflow for the ingestion of geospatial data sets and the development of an iVR workbench,and present the application of these for an experience of Iceland’s Thrihnukar volcano where we:(1)combined satellite imagery with terrain elevation data to create a basic reconstruction of the physical site;(2)used terrestrial LiDAR data to provide a geo-referenced point cloud model of the magmatic-volcanic system,as well as the LiDAR intensity values for the identification of rock types;and(3)used Structure-from-Motion(SfM)to construct a photorealistic point cloud of the inside volcano.The workbench provides tools for the direct manipulation of the georeferenced data sets,including scaling,rotation,and translation,and a suite of geometric measurement tools,including length,area,and volume.Future developments will be inspired by an ongoing user study that formally evaluates the workbench’s mature components in the context of fieldwork and analyses activities.
基金the National Natural Science Foundation of China(No.61133014)
文摘In current cloud computing system, large amounts of sensitive data are shared to other cloud users. To keep these data confidentiality, data owners should encrypt their data before outsourcing. We choose proxy reencryption （PRE） as the cloud data encryption technique. In a PRE system, a semi-trusted proxy can transform a ciphertext under one public key into a ciphertext of the same message under another public key, but the proxy cannot gain any information about the message. In this paper, we propose a certificateless PRE （CL-PRE） scheme without pairings. The security of the proposed scheme can be proved to be equivalent to the computational Dire- Hellman （CDH） problem in the random oracle model. Compared with other existing CL-PRE schemes, our scheme requires less computation cost and is significantly more efficient. The new scheme does not need the public key certificates to guarantee validity of public keys and solves the key escrow problem in identity-based public key cryptography.
基金This work was supported by the National Key R&D Program of China(No.2018YFB1700100)the National Natural Science Foundation of China(No.61873317)。
文摘To address the private data management problems and realize privacy-preserving data sharing,a blockchain-based transaction system named Ecare featuring information transparency,fairness and scalability is proposed.The proposed system formulates multiple private data access control strategies,and realizes data trading and sharing through on-chain transactions,which makes transaction records transparent and immutable.In our system,the private data are encrypted,and the role-based account model ensures that access to the data requires owner’s authorization.Moreover,a new consensus protocol named Proof of Transactions(PoT)proposed by ourselves has been used to improve consensus efficiency.The value of Ecare is not only that it aggregates telemedicine,data transactions,and other features,but also that it translates these actions into transaction events stored in the blockchain,making them transparent and immutable to all participants.The proposed system can be extended to more general big data privacy protection and data transaction scenarios.
文摘With the rapid growth of Internet of Things(IoT)based models,and the lack amount of data makes cloud computing resources insufficient.Hence,edge computing-based techniques are becoming more popular in present research domains that makes data storage,and processing effective at the network edges.There are several advanced features like parallel processing and data perception are available in edge computing.Still,there are some challenges in providing privacy and data security over networks.To solve the security issues in Edge Computing,Hash-based Message Authentication Code(HMAC)algorithm is used to provide solutions for preserving data from various attacks that happens with the distributed network nature.This paper proposed a Trust Model for Secure Data Sharing(TM-SDS)with HMAC algorithm.Here,data security is ensured with local and global trust levels with the centralized processing of cloud and by conserving resources effectively.Further,the proposed model achieved 84.25%of packet delivery ratio which is better compared to existing models in the resulting phase.The data packets are securely transmitted between entities in the proposed model and results showed that proposed TM-SDS model outperforms the existing models in an efficient manner.
文摘Big data has a strong demand for a network infrastructure with the capability to support data sharing and retrieval efficiently. Information-centric networking （ICN） is an emerging approach to satisfy this demand, where big data is cached ubiquitously in the network and retrieved using data names. However, existing authentication and authorization schemes rely mostly on centralized servers to provide certification and mediation services for data retrieval. This causes considerable traffic overhead for the secure distributed sharing of data. To solve this problem, we employ identity-based cryptography （IBC） to propose a Distributed Authentication and Authorization Scheme （DAAS）, where an identity-based signature （IBS） is used to achieve distributed verifications of the identities of publishers and users. Moreover, Ciphertext-Policy Attribnte-based encryption （CP-ABE） is used to enable the distributed and fine-grained authorization. DAAS consists of three phases： initialization, secure data publication, and secure data retrieval, which seamlessly integrate authentication and authorization with the in- terest/data communication paradigm in ICN. In particular, we propose trustworthy registration and Network Operator and Authority Manifest （NOAM） dissemination to provide initial secure registration and enable efficient authentication for global data retrieval. Meanwhile, Attribute Manifest （AM） distribution coupled with automatic attribute update is proposed to reduce the cost of attribute retrieval. We examine the performance of the proposed DAAS, which shows that it can achieve a lower bandwidth cost than existing schemes.
基金supported by the National Key Basic Research Program of China (973 Program) under Grant No. 2009CB320505the Fundamental Research Funds for the Central Universities under Grant No. 2011RC0508+2 种基金the National Natural Science Foundation of China under Grant No. 61003282China Next Generation Internet Project "Research and Trial on Evolving Next Generation Network Intelligence Capability Enhancement"the National Science and Technology Major Project "Research about Architecture of Mobile Internet" under Grant No. 2011ZX03002-001-01
文摘This paper aims to find a practical way of quantitatively representing the privacy of network data. A method of quantifying the privacy of network data anonymization based on similarity distance and entropy in the scenario involving multiparty network data sharing with Trusted Third Party (TTP) is proposed. Simulations are then conducted using network data from different sources, and show that the measurement indicators defined in this paper can adequately quantify the privacy of the network. In particular, it can indicate the effect of the auxiliary information of the adversary on privacy.
基金Supported by Ministry of Science and Technology"National Science and Technology Platform Program"(2005DKA31800)
文摘Science data are very important resources for innovative research in all scientific disciplines. The Ministry of Science and Technology （MOST） of China has launched a comprehensive platform program for supporting scientific innovations and agricultural science database construction and sharing project is one of the activities under this program supported by MOST. This paper briefly described the achievements of the Agricultural Science Data Center Project.
文摘With the advancements in the era of artificial intelligence,blockchain,cloud computing,and big data,there is a need for secure,decentralized medical record storage and retrieval systems.While cloud storage solves storage issues,it is challenging to realize secure sharing of records over the network.Medi-block record in the healthcare system has brought a new digitalization method for patients’medical records.This centralized technology provides a symmetrical process between the hospital and doctors when patients urgently need to go to a different or nearby hospital.It enables electronic medical records to be available with the correct authentication and restricts access to medical data retrieval.Medi-block record is the consumer-centered healthcare data system that brings reliable and transparent datasets for the medical record.This study presents an extensive review of proposed solutions aiming to protect the privacy and integrity of medical data by securing data sharing for Medi-block records.It also aims to propose a comprehensive investigation of the recent advances in different methods of securing data sharing,such as using Blockchain technology,Access Control,Privacy-Preserving,Proxy Re-Encryption,and Service-On-Chain approach.Finally,we highlight the open issues and identify the challenges regarding secure data sharing for Medi-block records in the healthcare systems.
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 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 by the Ministry of Education Industry-University Cooperation Collaborative Education Projects of China under Grant 202102119036 and 202102082013。
文摘Data sharing technology in Internet of Vehicles(Io V)has attracted great research interest with the goal of realizing intelligent transportation and traffic management.Meanwhile,the main concerns have been raised about the security and privacy of vehicle data.The mobility and real-time characteristics of vehicle data make data sharing more difficult in Io V.The emergence of blockchain and federated learning brings new directions.In this paper,a data-sharing model that combines blockchain and federated learning is proposed to solve the security and privacy problems of data sharing in Io V.First,we use federated learning to share data instead of exposing actual data and propose an adaptive differential privacy scheme to further balance the privacy and availability of data.Then,we integrate the verification scheme into the consensus process,so that the consensus computation can filter out low-quality models.Experimental data shows that our data-sharing model can better balance the relationship between data availability and privacy,and also has enhanced security.
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.
基金supported by the National Natural Science Foundation of China under Grant 61972148。
文摘The traditional centralized data sharing systems have potential risks such as single point of failures and excessive working load on the central node.As a distributed and collaborative alternative,approaches based upon blockchain have been explored recently for Internet of Things(IoTs).However,the access from a legitimate user may be denied without the pre-defined policy and data update on the blockchain could be costly to the owners.In this paper,we first address these issues by incorporating the Accountable Subgroup Multi-Signature(ASM)algorithm into the Attribute-based Access Control(ABAC)method with Policy Smart Contract,to provide a finegrained and flexible solution.Next,we propose a policy-based Chameleon Hash algorithm that allows the data to be updated in a reliable and convenient way by the authorized users.Finally,we evaluate our work by comparing its performance with the benchmarks.The results demonstrate significant improvement on the effectiveness and efficiency.
基金supported by the China Postdoctoral Science Foundation (Grant Nos. 2021TQ0042, 2021M700435, 2021TQ0041)the National Natural Science Foundation of China (Grant No. 62102027)the Shandong Provincial Key Research and Development Program (2021CXGC010106)
文摘In this paper,we propose a novel fuzzy matching data sharing scheme named FADS for cloudedge communications.FADS allows users to specify their access policies,and enables receivers to obtain the data transmitted by the senders if and only if the two sides meet their defined certain policies simultaneously.Specifically,we first formalize the definition and security models of fuzzy matching data sharing in cloud-edge environments.Then,we construct a concrete instantiation by pairing-based cryptosystem and the privacy-preserving set intersection on attribute sets from both sides to construct a concurrent matching over the policies.If the matching succeeds,the data can be decrypted.Otherwise,nothing will be revealed.In addition,FADS allows users to dynamically specify the policy for each time,which is an urgent demand in practice.A thorough security analysis demonstrates that FADS is of provable security under indistinguishable chosen ciphertext attack(IND-CCA)in random oracle model against probabilistic polynomial-time(PPT)adversary,and the desirable security properties of privacy and authenticity are achieved.Extensive experiments provide evidence that FADS is with acceptable efficiency.
文摘An M_(S)7.4 earthquake struck west China in Maduo county,Guoluo prefecture,Qinghai province on May 22,2021,at 2:04 Beijing time(18:04 UTC on May 21,2021),which broke the quiet period of Chinese mainland for 1382 days without earthquakes of magnitude 7 or higher.The analysis of the seismic data sequence would play an important role in the in-depth study of the Maduo earthquake and the Bayan Har block.The Institute of Geophysics,China Earthquake Administration(CEA),compiled observation data recorded through 57 broadband seismometers within 500 km of the earthquake epicenter and intended to share for further researches in earthquake science community.The shared dataset included waveforms of the event and its sequence with magnitudes of 3.0 or higher that occurred between May 22-31,2021 with a sampling rate of 100 sps along with the continuous waveforms of 20 Hz and 100 Hz.Additionally,the seismic instrument response files also were shared.The event and continuous waveform records could be downloaded by submitting a request through the web platform of the Earthquake Science Data Center of the Institute of Geophysics,CEA(www.esdc.ac.cn).
基金supported in part by National High-Tech Research and Development Program of China(“863”Program)under Grant No.2015AA016004National Natural Science Foundation of China under Grants No.61173154,61272451,61572380
文摘Data sharing is a main application of cloud computing. Some existing solutions are proposed to provide flexible access control for outsourced data in the cloud. However, few attentions have been paid to group-oriented data sharing when multiple data owners want to share their private data for cooperative purposes. In this paper, we put forward a new paradigm, referred to as secure, scalable and efficient multi-owner(SSEM) data sharing in clouds. The SSEM integrates identity-based encryption and asymmetric group key agreement to enable group-oriented access control for data owners in a many-to-many sharing pattern. Moreover, with SSEM, users can join in or leave from the group conveniently with the privacy of both group data and user data.We proposed the key-ciphertext homomorphism technique to construct an SSEM scheme with short ciphertexts. The security analysis shows that our SSEM scheme achieves data security against unauthorized accesses and collusion attacks. Both theoretical and experimental results confirm that our proposed scheme takes users little costs to share and access outsourced data in a group manner.
基金The National Social Science Foundation of China(No.17BGL196)。
文摘Due to the fact that consumers'privacy data sharing has multifaceted and complex effects on the e-commerce platform and its two sided agents,consumers and sellers,a game-theoretic model in a monopoly e-market is set up to study the equilibrium strategies of the three agents(the platform,the seller on it and consumers)under privacy data sharing.Equilibrium decisions show that after sharing consumers'privacy data once,the platform can collect more privacy data from consumers.Meanwhile,privacy data sharing pushes the seller to reduce the product price.Moreover,the platform will increase the transaction fee if the privacy data sharing value is high.It is also indicated that privacy data sharing always benefits consumers and the seller.However,the platform's profit decreases if the privacy data sharing value is low and the privacy data sharing level is high.Finally,an extended model considering an incomplete information game among the agents is discussed.The results show that both the platform and the seller cannot obtain a high profit from privacy data sharing.Factors including the seller's possibility to buy privacy data,the privacy data sharing value and privacy data sharing level affect the two agents'payoffs.If the platform wishes to benefit from privacy data sharing,it should increase the possibility of the seller to buy privacy data or increase the privacy data sharing value.
