FADSF:A Data Sharing Model for Intelligent Connected Vehicles Based on Blockchain Technology

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.

A Blind Batch Encryption and Public Ledger-Based Protocol for Sharing Sensitive Data

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.

Blockchain-Enabled Federated Learning for Privacy-Preserving Non-IID Data Sharing in Industrial Internet

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.

A General Framework for Intelligent IoT Data Acquisition and Sharing in an Untrusted Environment Based on Blockchain

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.

Cross-Domain Bilateral Access Control on Blockchain-Cloud Based Data Trading System

Data trading enables data owners and data requesters to sell and purchase data.With the emergence of blockchain technology,research on blockchain-based data trading systems is receiving a lot of attention.Particularly,to reduce the on-chain storage cost,a novel paradigm of blockchain and cloud fusion has been widely considered as a promising data trading platform.Moreover,the fact that data can be used for commercial purposes will encourage users and organizations from various fields to participate in the data marketplace.In the data marketplace,it is a challenge how to trade the data securely outsourced to the external cloud in a way that restricts access to the data only to authorized users across multiple domains.In this paper,we propose a cross-domain bilateral access control protocol for blockchain-cloud based data trading systems.We consider a system model that consists of domain authorities,data senders,data receivers,a blockchain layer,and a cloud provider.The proposed protocol enables access control and source identification of the outsourced data by leveraging identity-based cryptographic techniques.In the proposed protocol,the outsourced data of the sender is encrypted under the target receiver’s identity,and the cloud provider performs policy-match verification on the authorization tags of the sender and receiver generated by the identity-based signature scheme.Therefore,data trading can be achieved only if the identities of the data sender and receiver simultaneously meet the policies specified by each other.To demonstrate efficiency,we evaluate the performance of the proposed protocol and compare it with existing studies.

A Cloud-Fog Enabled and Privacy-Preserving IoT Data Market Platform Based on Blockchain

The dynamic landscape of the Internet of Things(IoT)is set to revolutionize the pace of interaction among entities,ushering in a proliferation of applications characterized by heightened quality and diversity.Among the pivotal applications within the realm of IoT,as a significant example,the Smart Grid(SG)evolves into intricate networks of energy deployment marked by data integration.This evolution concurrently entails data interchange with other IoT entities.However,there are also several challenges including data-sharing overheads and the intricate establishment of trusted centers in the IoT ecosystem.In this paper,we introduce a hierarchical secure data-sharing platform empowered by cloud-fog integration.Furthermore,we propose a novel non-interactive zero-knowledge proof-based group authentication and key agreement protocol that supports one-to-many sharing sets of IoT data,especially SG data.The security formal verification tool shows that the proposed scheme can achieve mutual authentication and secure data sharing while protecting the privacy of data providers.Compared with previous IoT data sharing schemes,the proposed scheme has advantages in both computational and transmission efficiency,and has more superiority with the increasing volume of shared data or increasing number of participants.

The accessible seismological dataset of a high-density 2D seismic array along Anninghe fault

The scientific goal of the Anninghe seismic array is to investigate the detailed geometry of the Anninghe fault and the velocity structure of the fault zone.This 2D seismic array is composed of 161 stations forming sub-rectangular geometry along the Anninghe fault,which covers 50 km and 150 km in the fault normal and strike directions,respectively,with~5 km intervals.The data were collected between June 2020 and June 2021,with some level of temporal gaps.Two types of instruments,i.e.QS-05A and SmartSolo,are used in this array.Data quality and examples of seismograms are provided in this paper.After the data protection period ends(expected in June 2024),researchers can request a dataset from the National Earthquake Science Data Center.

A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification

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.

Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes

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.

Federated Learning with Blockchain for Privacy-Preserving Data Sharing in Internet of Vehicles

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.

BC-PC-Share:Blockchain-Based Patient-Centric Data Sharing Scheme for PHRs in Cloud Computing

Sharing of personal health records(PHR)in cloud computing is an essential functionality in the healthcare system.However,how to securely,efficiently and flexibly share PHRs data of the patient in a multi-receiver setting has not been well addressed.For instance,since the trust domain of the cloud server is not identical to the data owner or data user,the semi-trust cloud service provider may intentionally destroy or tamper shared PHRs data of user or only transform partial ciphertext of the shared PHRs or even return wrong computation results to save its storage and computation resource,to pursue maximum economic interest or other malicious purposes.Thus,the PHRs data storing or sharing via the cloud server should be performed with consistency and integrity verification.Fortunately,the emergence of blockchain technology provides new ideas and prospects for ensuring the consistency and integrity of shared PHRs data.To this end,in this work,we leverage the consortiumblockchain technology to enhance the trustworthiness of each participant and propose a blockchain-based patient-centric data sharing scheme for PHRs in cloud computing(BC-PC-Share).Different from the state-of-art schemes,our proposal can achieve the following desired properties:(1)Realizing patient-centric PHRs sharing with a public verification function,i.e.,which can ensure that the returned shared data is consistent with the requested shared data and the integrity of the shared data is not compromised.(2)Supporting scalable and fine-grained access control and sharing of PHRs data with multiple domain users,such as hospitals,medical research institutes,and medical insurance companies.(3)Achieving efficient user decryption by leveraging the transformation key technique and efficient user revocation by introducing time-controlled access.The security analysis and simulation experiment demonstrate that the proposed BC-PC-Share scheme is a feasible and promising solution for PHRs data sharing via consortium blockchain.

Medi-Block Record Secure Data Sharing in Healthcare System:Issues,Solutions and Challenges

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.

Research on the Electric Energy Metering Data Sharing Method in Smart Grid Based on Blockchain

Enabling data sharing among smart grid power suppliers is a pressing challenge due to technical hurdles in verifying,storing,and synchronizing energy metering data.Access and sharing limitations are stringent for users,power companies,and researchers,demanding significant resources and time for permissions and verification.This paper proposes a blockchain-based architecture for secure and efficient sharing of electric energy metering data.Further,we propose a data sharing model based on evolutionary game theory.Based on the Lyapunov stability theory,the model’s evolutionary stable strategy(ESS)is analyzed.Numerical results verify the correctness and practicability of the scheme proposed in this paper,and provide a new method for realizing convenient,safe and fast data sharing.

VKFQ:A Verifiable Keyword Frequency Query Framework with Local Differential Privacy in Blockchain

With its untameable and traceable properties,blockchain technology has been widely used in the field of data sharing.How to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data sharing.In this paper,we study verifiable keyword frequency(KF)queries with local differential privacy in blockchain.Both the numerical and the keyword attributes are present in data objects;the latter are sensitive and require privacy protection.However,prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF queries.We propose an efficient framework that protects data owners’privacy on keyword attributes while enabling quick and verifiable query processing for KF queries.The framework computes an estimate of a keyword’s frequency and is efficient in query time and verification object(VO)size.A utility-optimized local differential privacy technique is used for privacy protection.The data owner adds noise locally into data based on local differential privacy so that the attacker cannot infer the owner of the keywords while keeping the difference in the probability distribution of the KF within the privacy budget.We propose the VB-cm tree as the authenticated data structure(ADS).The VB-cm tree combines the Verkle tree and the Count-Min sketch(CM-sketch)to lower the VO size and query time.The VB-cm tree uses the vector commitment to verify the query results.The fixed-size CM-sketch,which summarizes the frequency of multiple keywords,is used to estimate the KF via hashing operations.We conduct an extensive evaluation of the proposed framework.The experimental results show that compared to theMerkle B+tree,the query time is reduced by 52.38%,and the VO size is reduced by more than one order of magnitude.

Secure and Efficient Data Storage and Sharing Scheme Based on Double Blockchain

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.

Achieving Fuzzy Matching Data Sharing for Secure Cloud-Edge Communication

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.

Achieving Fine-Grained and Flexible Access Control on Blockchain-Based Data Sharing for the Internet of Things

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.

An intelligent and privacy-enhanced data sharing strategy for blockchain-empowered Internet of Things

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.

Developing a Geological Management Information System: National Important Mining Zone Database

Geo-data is a foundation for the prediction and assessment of ore resources, so managing and making full use of those data, including geography database, geology database, mineral deposits database, aeromagnetics database, gravity database, geochemistry database and remote sensing database, is very significant. We developed national important mining zone database （NIMZDB） to manage 14 national important mining zone databases to support a new round prediction of ore deposit. We found that attention should be paid to the following issues： ① data accuracy： integrity, logic consistency, attribute, spatial and time accuracy; ② management of both attribute and spatial data in the same system;③ transforming data between MapGIS and ArcGIS; ④ data sharing and security; ⑤ data searches that can query both attribute and spatial data. Accuracy of input data is guaranteed and the search, analysis and translation of data between MapGIS and ArcGIS has been made convenient via the development of a checking data module and a managing data module based on MapGIS and ArcGIS. Using AreSDE, we based data sharing on a client/server system, and attribute and spatial data are also managed in the same system.

Prompt seismic data sharing for the 2021 Maduo earthquake in Qinghai province, China

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).