2,035 research outputs found

    Data trust framework using blockchain and smart contracts

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    Lack of trust is the main barrier preventing more widespread data sharing. The lack of transparent and reliable infrastructure for data sharing prevents many data owners from sharing their data. Data trust is a paradigm that facilitates data sharing by forcing data controllers to be transparent about the process of sharing and reusing data. Blockchain technology has the potential to present the essential properties for creating a practical and secure data trust framework by transforming current auditing practices and automatic enforcement of smart contracts logic without relying on intermediaries to establish trust. Blockchain holds an enormous potential to remove the barriers of traditional centralized applications and propose a distributed and transparent administration by employing the involved parties to maintain consensus on the ledger. Furthermore, smart contracts are a programmable component that provides blockchain with more flexible and powerful capabilities. Recent advances in blockchain platforms toward smart contracts' development have revealed the possibility of implementing blockchain-based applications in various domains, such as health care, supply chain and digital identity. This dissertation investigates the blockchain's potential to present a framework for data trust. It starts with a comprehensive study of smart contracts as the main component of blockchain for developing decentralized data trust. Interrelated, three decentralized applications that address data sharing and access control problems in various fields, including healthcare data sharing, business process, and physical access control system, have been developed and examined. In addition, a general-purpose application based on an attribute-based access control model is proposed that can provide trusted auditability required for data sharing and access control systems and, ultimately, a data trust framework. Besides auditing, the system presents a transparency level that both access requesters (data users) and resource owners (data controllers) can benefit from. The proposed solutions have been validated through a use case of independent digital libraries. It also provides a detailed performance analysis of the system implementation. The performance results have been compared based on different consensus mechanisms and databases, indicating the system's high throughput and low latency. Finally, this dissertation presents an end-to-end data trust framework based on blockchain technology. The proposed framework promotes data trustworthiness by assessing input datasets, effectively managing access control, and presenting data provenance and activity monitoring. A trust assessment model that examines the trustworthiness of input data sets and calculates the trust value is presented. The number of transaction validators is defined adaptively with the trust value. This research provides solutions for both data owners and data users’ by ensuring the trustworthiness and quality of the data at origin and transparent and secure usage of the data at the end. A comprehensive experimental study indicates the presented system effectively handles a large number of transactions with low latency

    Audit and Certification of Digital Repositories: Creating a Mandate for the Digital Curation Centre (DCC)

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    The article examines the issues surrounding the audit and certification of digital repositories in light of the work that the RLG/NARA Task Force did to draw up guidelines and the need for these guidelines to be validated.

    Survey On Ensuring Distributed Accountability for Data Sharing in the Cloud

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    Cloud computing is the use of computing of sources that are delivered as a service over a network for example on internet. It enables highly scalable services to be easily utilized over the Internet on an as needed basis. Important characteristic of the cloud services is that users’ data are usually processed remotely in unknown machines that users do not operate. It can become a substantial barrier to the wide taking on cloud services. To address this problem highly decentralized responsibility framework to keep track of the actual usage of the user’s data in the cloud. In this work has automated logging and distributed auditing mechanism. The Cloud Information Accountability framework proposed in this work conducts distributed auditing of relevant access performed by any entity, carried out at any point of time at any cloud service provider. It conations two major elements: logger and log harmonizer. This methodology will also take concern of the JAR file by converting the JAR into obfuscated code which will adds an additional layer of security to the infrastructure. Rather than this here in this work, increase the security of user’s data by provable data control for integrity verificatio

    HathiTrust Research Center: Computational Research on the HathiTrust Repository

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    PIs (exec mgt team): Beth A. Plale, Indiana University; Marshall Scott Poole, University of Illinois Urbana-Champaign ; Robert McDonald, IU; John Unsworth (UIUC) Senior investigators: Loretta Auvil (UIUC); Johan Bollen (IU), Randy Butler (UIUC); Dennis Cromwell (IU), Geoffrey Fox (IU), Eileen Julien (IU), Stacy Kowalczyk (IU); Danny Powell (UIUC); Beth Sandore (UIUC); Craig Stewart (IU); John Towns (UIUC); Carolyn Walters (IU), Michael Welge (UIUC); Eric Wernert (IU

    Big Data Security (Volume 3)

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    After a short description of the key concepts of big data the book explores on the secrecy and security threats posed especially by cloud based data storage. It delivers conceptual frameworks and models along with case studies of recent technology

    Data Provenance and Management in Radio Astronomy: A Stream Computing Approach

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    New approaches for data provenance and data management (DPDM) are required for mega science projects like the Square Kilometer Array, characterized by extremely large data volume and intense data rates, therefore demanding innovative and highly efficient computational paradigms. In this context, we explore a stream-computing approach with the emphasis on the use of accelerators. In particular, we make use of a new generation of high performance stream-based parallelization middleware known as InfoSphere Streams. Its viability for managing and ensuring interoperability and integrity of signal processing data pipelines is demonstrated in radio astronomy. IBM InfoSphere Streams embraces the stream-computing paradigm. It is a shift from conventional data mining techniques (involving analysis of existing data from databases) towards real-time analytic processing. We discuss using InfoSphere Streams for effective DPDM in radio astronomy and propose a way in which InfoSphere Streams can be utilized for large antennae arrays. We present a case-study: the InfoSphere Streams implementation of an autocorrelating spectrometer, and using this example we discuss the advantages of the stream-computing approach and the utilization of hardware accelerators

    Designing Data Governance in Platform Ecosystems

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    As platform ecosystems such as Facebook or Twitter are rapidly growing through platform users’ data contribution, the importance of data governance has been highlighted. Platform ecosystems, however, face increasing complexity derived from the business context such as multiple parties’ participation. How to share control and decision rights about data assets with platform users is regarded as a significant governance design issue. However, there is a lack of studies on this issue. Existing design models focus on the characteristics of enterprises. Therefore, there is limited support for platform ecosystems where there are different types of context and complicated relationships. To deal with the issue, this paper proposes a novel design approach for data governance in platform ecosystems including design principles, contingency factors and an architecture model. Case studies are performed to illustrate the practical implications of our suggestion
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