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 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.

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.

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 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.

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.

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.

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.

Mendeley Data平台开放科学数据实践及启示

高效的研究数据集成发现和管理工具推动研究数据的开放共享。本文以Elsevier公司Mendeley Data科研数据管理与共享平台为研究对象,从数据治理流程、集成的开放生态系统、数据存储管理和共享功能方面进行调研,分析、总结该平台开放数据实践中围绕数据标准在流程设计和系统开发方面的优势,为我国高校图书馆、研究型图书馆以及信息服务机构开展开放数据资源建设和研究数据服务提供参考借鉴。

基于全球研究数据注册仓储Re3data.org的医学科学数据仓储建设

目的:总结全球医学领域科学数据仓储建设现状、经验与不足,为我国医学科学数据仓储建设提供参考和借鉴。方法:利用统计分析、共现分析和社会网络分析等方法,结合可视化图表对2018年Re3data.org中收录的637个医学科学数据仓储的分布、建设等情况进行分析。结果:美国、英国和国际组织在仓储合作建设中贡献最为突出,其中美国参与建设的数据仓储占50%,独立建设仓储达241个。多数仓储收录3～6类数据,科学和统计数据是被收录最多的类型,收录数据主题丰富,64.36%的仓储提供数据质控。Dspace等7种软件被用于数据仓储建设,数据管理涉及5种唯一标识符、16种元数据。结论:欧美国家占据了数据仓储建设高地,需多方合作共促数据开放共享。开源技术降低了仓储搭建门槛,标准规范建设保障仓储运管,数据分级共享提供接口支持,政策许可可保障多方权益。

EOS Data Dumper——EOS免费数据自动下载与重发布系统

为了更有效的利用已有数据资源,不造成科研设施的重复投资,数据共享越来越受到重视.NASA对地观测系统(EOS)提供了大量的包括MODIS在内的免费数据资源,为此,EOS Data Dumper(EDD)通过程序模拟EOS数据门户的正常下载流程,采用了先进的Web页面文本信息捕捉技术,实现定时自动下载研究区的全部EOS免费数据,并通过免费的DIAL系统,向互联网重新发布,实现复杂的基于时空的数据查询.从技术角度详细介绍了EDD的项目背景与意义、实现方案。

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).

Shared-nothing并行事务数据库系统中规则的挖掘与更新算法

关联规则是数据挖掘中的一个重要研究内容 .本文提出了 Shared- nothing并行事务数据库系统 (简称SNPDBS)中一种快速的关联规则挖掘算法 SNPMAR,并考虑当最小支持度发生变化后 SNPDBS中关联规则的高效更新问题 ,提出了一种有效的关联规则更新算法 SNPIUA.

数字地球建设中的地理空间Metadata研究

为解决数字地球建设中现有海量地理空间信息的共建共享、有效管理和利用 ,结合国际地理空间Metadata标准规范 ,对数字地球建设中所需的地理空间Metadata的定义、作用、分类、内容、实现模型与实例及其应用进行详细研究和设计 ,提出用于数字地球建设的地理空间Metadata网络管理系统解决方案 ,并设计实现了原型系统 ,为数字地球建设中现有海量地理空间信息的有效管理和共建共享提供了理论基础和切实可行的技术支撑。对国家空间信息基础设施建设与数字地球建设具有重要的推动作用和深远的社会意义。

An accessible seismological dataset of 2021 Yangbi M_(S)6.4 earthquake

A M_(S)6.4 earthquake occurred on 21 May 2021 in Yangbi county,Dali prefecture,Yunnan,China,at 21:48 Beijing Time(13:48 UTC).Earthquakes with an M3.0 or higher occurred before and after the main shock.Seismic data analysis is essential for the in-depth investigation of the 2021 Yangbi M_(S)6.4 earthquake sequence and the seismotectonics of northwestern Yunnan.Institute of Geophysics,China Earthquake Administration(CEA),has compiled a dataset of seismological observations from 157 broadband stations located within 500 km of the epicenter,and has made this dataset available to the earthquake science research community.The dataset(total file size:329 GB)consists of event waveforms with a sampling frequency of 100 sps collected from 18 to 28 May 2021,20-Hz and 100-Hz continuous waveforms collected from 12 to 31 May 2021,and seismic instrument response files.To promote data sharing,the dataset also includes the seismic event waveforms from 20 to 22 May 2021 recorded at 50 stations of the ongoing Binchuan Active Source Geophysical Observation Project,for which the data protection period has not expired.Sample waveforms of the main shock are included in the appendix of this article and can be downloaded from the Earthquake Science website.The event and continuous waveforms are available from the Earthquake Science Data Center website(www.esdc.ac.cn)on application.

哈佛大学Datatags数据分级系统研究及启示

在数据密集型研究范式背景下,科学数据共享中的数据安全问题得到前所未有的重视。数据分级成为普遍采取的数据安全共享措施之一。通过梳理国内外科学数据分级研究和发展现状,本文对哈佛大学Datatags数据分级系统进行了研究,全面分析其系统架构原理、数据分级思路、分级模型和应用案例,以期为我国科学数据安全共享研究和实践提供新思路。

Flash mx2004中利用SharedObject存取本地磁盘数据

探讨了在FlashMx2004中利用共享对象实现对本地数据进行存取的原理,并且用一个实例来说明实现存取数据的具体的步骤和方法。

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.

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.