scholarly journals FaDe: A Blockchain-Based Fair Data Exchange Scheme for Big Data Sharing

2019 ◽  
Vol 11 (11) ◽  
pp. 225 ◽  
Author(s):  
Yuling Chen ◽  
Jinyi Guo ◽  
Changlou Li ◽  
Wei Ren
Keyword(s):  
Big Data ◽  
Data Sharing ◽  
Data Privacy ◽  
Data Exchange ◽  
Data Dissemination ◽  
Third Party ◽  
Management Efficiency ◽  
Centralized Management ◽  
Efficiency Data ◽  
Critical Resources

In the big data era, data are envisioned as critical resources with various values, e.g., business intelligence, management efficiency, and financial evaluations. Data sharing is always mandatory for value exchanges and profit promotion. Currently, certain big data markets have been created for facilitating data dissemination and coordinating data transaction, but we have to assume that such centralized management of data sharing must be trustworthy for data privacy and sharing fairness, which very likely imposes limitations such as joining admission, sharing efficiency, and extra costly commissions. To avoid these weaknesses, in this paper, we propose a blockchain-based fair data exchange scheme, called FaDe. FaDe can enable de-centralized data sharing in an autonomous manner, especially guaranteeing trade fairness, sharing efficiency, data privacy, and exchanging automation. A fairness protocol based on bit commitment is proposed. An algorithm based on blockchain script architecture for a smart contract, e.g., by a bitcoin virtual machine, is also proposed and implemented. Extensive analysis justifies that the proposed scheme can guarantee data exchanging without a trusted third party fairly, efficiently, and automatically.

Secure Data Dissemination

2008 ◽  
pp. 1839-1864
Author(s):  
Elisa Bertino ◽  
Barbara Carminati ◽  
Elena Ferrari
Keyword(s):  
Digital Signature ◽  
Data Exchange ◽  
Data Dissemination ◽  
Third Party ◽  
New Paradigm ◽  
Security Issues ◽  
Security Properties ◽  
Verification Process ◽  
The Web ◽  
User Queries

In this chapter, we present the main security issues related to the selective dissemination of information (SDI system). More precisely, after provided an overview of the work carried out in this field, we have focused on the security properties that a secure SDI system (SSDI system) must satisfy and on some of the strategies and mechanisms that can be used to ensure them.  Indeed, since XML is the today emerging standard for data exchange over the Web, we have casted our attention on Secure and Selective XML data dissemination (SSXD).  As a result, we have presented a SSXD system providing a comprehensive solution to XML documents. In the proposed chapter, we also consider innovative architecture for the data dissemination, by suggesting a SSXD system exploiting the third-party architecture, since this architecture is receiving growing attention as a new paradigm for data dissemination over the web. In a third-party architecture, there is a distinction between the  Owner  and the Publisher of information. The Owner is the producer of the information, whereas Publishers are responsible for managing (a portion of) the Owner information and for answering user queries. A relevant issue in this architecture is how the Owner can ensure a secure dissemination of its data, even if the data are managed by a third-party. Such scenario requires a redefinition of dissemination mechanisms developed for the traditional SSXD system. Indeed, the traditional techniques cannot be exploited in a third party scenario. For instance, let us consider the traditional digital signature techniques, used to ensure data integrity and authenticity. In a third party scenario, that is, a scenario where a third party may prune some of the nodes of the original document based on user queries, the traditional digital signature is not applicable, since its correctness is based on the requirement that the signing and verification process are performed on exactly the same bits.

Differential Privacy Approach for Big Data Privacy in Healthcare

2017 ◽  
pp. 191-213 ◽  
Author(s):  
Marmar Moussa ◽  
Steven A. Demurjian
Keyword(s):  
Big Data ◽  
Data Sharing ◽  
Data Privacy ◽  
Large Scale ◽  
Differential Privacy ◽  
Security And Privacy ◽  
Instrumental Case Study ◽  
Large Scale Data ◽  
Private Data

This chapter presents a survey of the most important security and privacy issues related to large-scale data sharing and mining in big data with focus on differential privacy as a promising approach for achieving privacy especially in statistical databases often used in healthcare. A case study is presented utilizing differential privacy in healthcare domain, the chapter analyzes and compares the major differentially private data release strategies and noise mechanisms such as the Laplace and the exponential mechanisms. The background section discusses several security and privacy approaches in big data including authentication and encryption protocols, and privacy preserving techniques such as k-anonymity. Next, the chapter introduces the differential privacy concepts used in the interactive and non-interactive data sharing models and the various noise mechanisms used. An instrumental case study is then presented to examine the effect of applying differential privacy in analytics. The chapter then explores the future trends and finally, provides a conclusion.

scholarly journals Flexible, Secure, and Reliable Data Sharing Service Based on Collaboration in Multicloud Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Qiang Wei ◽  
Huaibin Shao ◽  
Gongxuan Zhang
Keyword(s):  
Data Sharing ◽  
Data Exchange ◽  
Keyword Search ◽  
Service Providers ◽  
High Reliability ◽  
Economic Benefits ◽  
Reliable Data ◽  
Third Party ◽  
Data Service ◽  
Real World Data

Due to the abundant storage resources and high reliability data service of cloud computing, more individuals and enterprises are motivated to outsource their data to public cloud platform and enable legal data users to search and download what they need in the outsourced dataset. However, in “Paid Data Sharing” model, some valuable data should be encrypted before outsourcing for protecting owner’s economic benefits, which is an obstacle for flexible application. Specifically, if the owner does not know who (user) will download which data files in advance and even does not know the attributes of user, he/she has to either remain online all the time or import a trusted third party (TTP) to distribute the file decryption key to data user. Obviously, making the owner always remain online is too inflexible, and wholly depending on the security of TTP is a potential risk. In this paper, we propose a flexible, secure, and reliable data sharing scheme based on collaboration in multicloud environment. For securely and instantly providing data sharing service even if the owner is offline and without TTP, we distribute all encrypted split data/key blocks together to multiple cloud service providers (CSPs), respectively. An elaborate cryptographic protocol we designed helps the owner verify the correctness of data exchange bills, which is directly related to the owner’s economic benefits. Besides, in order to support reliable data service, the erasure-correcting code technic is exploited for tolerating multiple failures among CSPs, and we offer a secure keyword search mechanism that makes the system more close to reality. Extensive security analyses and experiments on real-world data show that our scheme is secure and efficient.

scholarly journals Data sharing who is the owner? Who will use it?

2020 ◽  
pp. 38
Author(s):  
José Rafael Marques da Silva ◽  
Manuela Correia
Keyword(s):  
Big Data ◽  
Data Sharing ◽  
Data Privacy ◽  
White Paper

This topic suggests reading and analysing an article about big data and data sharing issues (http://dx.doi.org/10.1016/j.agsy.2017.01.023). Data Privacy and Use White Paper. (2017). AgGateway.

Blockchain-Based Data Market (BCBDM) Framework for Security and Privacy

2021 ◽  
pp. 186-205
Author(s):  
Shailesh Pancham Khapre ◽  
Chandramohan Dhasarathan ◽  
Puviyarasi T. ◽  
Sam Goundar
Keyword(s):  
Big Data ◽  
Data Sharing ◽  
Data Storage ◽  
Data Privacy ◽  
Security And Privacy ◽  
Research Progress ◽  
Distributed Data ◽  
Distributed Data Storage ◽  
Data Market ◽  
Privacy Issues

In the internet era, incalculable data is generated every day. In the process of data sharing, complex issues such as data privacy and ownership are emerging. Blockchain is a decentralized distributed data storage technology. The introduction of blockchain can eliminate the disadvantages of the centralized data market, but at the same time, distributed data markets have created security and privacy issues. It summarizes the industry status and research progress of the domestic and foreign big data trading markets and refines the nature of the blockchain-based big data sharing and circulation platform. Based on these properties, a blockchain-based data market (BCBDM) framework is proposed, and the security and privacy issues as well as corresponding solutions in this framework are analyzed and discussed. Based on this framework, a data market testing system was implemented, and the feasibility and security of the framework were confirmed.

Differential Privacy Approach for Big Data Privacy in Healthcare

Web Services ◽  
2019 ◽  
pp. 1623-1645
Author(s):  
Marmar Moussa ◽  
Steven A. Demurjian
Keyword(s):  
Big Data ◽  
Data Sharing ◽  
Data Privacy ◽  
Large Scale ◽  
Differential Privacy ◽  
Security And Privacy ◽  
Instrumental Case Study ◽  
Large Scale Data ◽  
Private Data

This chapter presents a survey of the most important security and privacy issues related to large-scale data sharing and mining in big data with focus on differential privacy as a promising approach for achieving privacy especially in statistical databases often used in healthcare. A case study is presented utilizing differential privacy in healthcare domain, the chapter analyzes and compares the major differentially private data release strategies and noise mechanisms such as the Laplace and the exponential mechanisms. The background section discusses several security and privacy approaches in big data including authentication and encryption protocols, and privacy preserving techniques such as k-anonymity. Next, the chapter introduces the differential privacy concepts used in the interactive and non-interactive data sharing models and the various noise mechanisms used. An instrumental case study is then presented to examine the effect of applying differential privacy in analytics. The chapter then explores the future trends and finally, provides a conclusion.

Secure Data Dissemination

2004 ◽  
pp. 198-229
Author(s):  
Elisa Berino ◽  
Barbara Carminati ◽  
Elena Ferrari
Keyword(s):  
Digital Signature ◽  
Data Exchange ◽  
Data Dissemination ◽  
Third Party ◽  
New Paradigm ◽  
Security Issues ◽  
Security Properties ◽  
Verification Process ◽  
The Web ◽  
User Queries

In this chapter, we present the main security issues related to the selective dissemination of information (SDI system). More precisely, after provided an overview of the work carried out in this field, we have focused on the security properties that a secure SDI system (SSDI system) must satisfy and on some of the strategies and mechanisms that can be used to ensure them.  Indeed, since XML is the today emerging standard for data exchange over the Web, we have casted our attention on Secure and Selective XML data dissemination (SSXD).  As a result, we have presented a SSXD system providing a comprehensive solution to XML documents. In the proposed chapter, we also consider innovative architecture for the data dissemination, by suggesting a SSXD system exploiting the third-party architecture, since this architecture is receiving growing attention as a new paradigm for data dissemination over the web. In a third-party architecture, there is a distinction between the  Owner  and the Publisher of information. The Owner is the producer of the information, whereas Publishers are responsible for managing (a portion of) the Owner information and for answering user queries. A relevant issue in this architecture is how the Owner can ensure a secure dissemination of its data, even if the data are managed by a third-party. Such scenario requires a redefinition of dissemination mechanisms developed for the traditional SSXD system. Indeed, the traditional techniques cannot be exploited in a third party scenario. For instance, let us consider the traditional digital signature techniques, used to ensure data integrity and authenticity. In a third party scenario, that is, a scenario where a third party may prune some of the nodes of the original document based on user queries, the traditional digital signature is not applicable, since its correctness is based on the requirement that the signing and verification process are performed on exactly the same bits.

scholarly journals A Federated Record Linkage Algorithm for Secure Medical Data Sharing

2021 ◽  
Author(s):  
Christian M. Heidt ◽  
Hauke Hund ◽  
Christian Fegeler
Keyword(s):  
Data Sharing ◽  
Record Linkage ◽  
Medical Records ◽  
Data Privacy ◽  
Medical Data ◽  
Third Party ◽  
Bloom Filters ◽  
Data Set ◽  
Optimal Weights ◽  
Linkage Algorithm

The process of consolidating medical records from multiple institutions into one data set makes privacy-preserving record linkage (PPRL) a necessity. Most PPRL approaches, however, are only designed to link records from two institutions, and existing multi-party approaches tend to discard non-matching records, leading to incomplete result sets. In this paper, we propose a new algorithm for federated record linkage between multiple parties by a trusted third party using record-level bloom filters to preserve patient data privacy. We conduct a study to find optimal weights for linkage-relevant data fields and are able to achieve 99.5% linkage accuracy testing on the Febrl record linkage dataset. This approach is integrated into an end-to-end pseudonymization framework for medical data sharing.

Issues in security and privacy of big data

2018 ◽  
Vol 7 (12) ◽  
pp. 1
Author(s):  
Shaveta Bhatia
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Approximate Method ◽  
Biomedical Research ◽  
Cyber Security ◽  
Data Science ◽  
Third Party ◽  
Security And Privacy ◽  
Security Threats ◽  
The Third

 The epoch of the big data presents many opportunities for the development in the range of data science, biomedical research cyber security, and cloud computing. Nowadays the big data gained popularity.  It also invites many provocations and upshot in the security and privacy of the big data. There are various type of threats, attacks such as leakage of data, the third party tries to access, viruses and vulnerability that stand against the security of the big data. This paper will discuss about the security threats and their approximate method in the field of biomedical research, cyber security and cloud computing.

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