Sharing Big Data Safely
Sharing Big Data Safely

Author: Ted Dunning

Publisher: "O'Reilly Media, Inc."

Published: 2015-09-15

Total Pages: 95

ISBN-13: 1491953632

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Many big data-driven companies today are moving to protect certain types of data against intrusion, leaks, or unauthorized eyes. But how do you lock down data while granting access to people who need to see it? In this practical book, authors Ted Dunning and Ellen Friedman offer two novel and practical solutions that you can implement right away. Ideal for both technical and non-technical decision makers, group leaders, developers, and data scientists, this book shows you how to: Share original data in a controlled way so that different groups within your organization only see part of the whole. You’ll learn how to do this with the new open source SQL query engine Apache Drill. Provide synthetic data that emulates the behavior of sensitive data. This approach enables external advisors to work with you on projects involving data that you can't show them. If you’re intrigued by the synthetic data solution, explore the log-synth program that Ted Dunning developed as open source code (available on GitHub), along with how-to instructions and tips for best practice. You’ll also get a collection of use cases. Providing lock-down security while safely sharing data is a significant challenge for a growing number of organizations. With this book, you’ll discover new options to share data safely without sacrificing security.

Big Data
Big Data

Author: Fei Hu

Publisher: CRC Press

Published: 2016-04-27

Total Pages: 449

ISBN-13: 1498734871

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Although there are already some books published on Big Data, most of them only cover basic concepts and society impacts and ignore the internal implementation details-making them unsuitable to R&D people. To fill such a need, Big Data: Storage, Sharing, and Security examines Big Data management from an R&D perspective. It covers the 3S desi

Principles of Big Data
Principles of Big Data

Author: Jules J. Berman

Publisher: Newnes

Published: 2013-05-20

Total Pages: 288

ISBN-13: 0124047246

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Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators. - Learn general methods for specifying Big Data in a way that is understandable to humans and to computers - Avoid the pitfalls in Big Data design and analysis - Understand how to create and use Big Data safely and responsibly with a set of laws, regulations and ethical standards that apply to the acquisition, distribution and integration of Big Data resources

Sharing Big Data Safely
Sharing Big Data Safely

Author: Ted Dunning

Publisher: "O'Reilly Media, Inc."

Published: 2015-09-15

Total Pages: 97

ISBN-13: 1491953640

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Many big data-driven companies today are moving to protect certain types of data against intrusion, leaks, or unauthorized eyes. But how do you lock down data while granting access to people who need to see it? In this practical book, authors Ted Dunning and Ellen Friedman offer two novel and practical solutions that you can implement right away. Ideal for both technical and non-technical decision makers, group leaders, developers, and data scientists, this book shows you how to: Share original data in a controlled way so that different groups within your organization only see part of the whole. You’ll learn how to do this with the new open source SQL query engine Apache Drill. Provide synthetic data that emulates the behavior of sensitive data. This approach enables external advisors to work with you on projects involving data that you can't show them. If you’re intrigued by the synthetic data solution, explore the log-synth program that Ted Dunning developed as open source code (available on GitHub), along with how-to instructions and tips for best practice. You’ll also get a collection of use cases. Providing lock-down security while safely sharing data is a significant challenge for a growing number of organizations. With this book, you’ll discover new options to share data safely without sacrificing security.

Streaming, Sharing, Stealing
Streaming, Sharing, Stealing

Author: Michael D. Smith

Publisher: MIT Press

Published: 2017-08-25

Total Pages: 229

ISBN-13: 0262534525

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How big data is transforming the creative industries, and how those industries can use lessons from Netflix, Amazon, and Apple to fight back. “[The authors explain] gently yet firmly exactly how the internet threatens established ways and what can and cannot be done about it. Their book should be required for anyone who wishes to believe that nothing much has changed.” —The Wall Street Journal “Packed with examples, from the nimble-footed who reacted quickly to adapt their businesses, to laggards who lost empires.” —Financial Times Traditional network television programming has always followed the same script: executives approve a pilot, order a trial number of episodes, and broadcast them, expecting viewers to watch a given show on their television sets at the same time every week. But then came Netflix's House of Cards. Netflix gauged the show's potential from data it had gathered about subscribers' preferences, ordered two seasons without seeing a pilot, and uploaded the first thirteen episodes all at once for viewers to watch whenever they wanted on the devices of their choice. In this book, Michael Smith and Rahul Telang, experts on entertainment analytics, show how the success of House of Cards upended the film and TV industries—and how companies like Amazon and Apple are changing the rules in other entertainment industries, notably publishing and music. We're living through a period of unprecedented technological disruption in the entertainment industries. Just about everything is affected: pricing, production, distribution, piracy. Smith and Telang discuss niche products and the long tail, product differentiation, price discrimination, and incentives for users not to steal content. To survive and succeed, businesses have to adapt rapidly and creatively. Smith and Telang explain how. How can companies discover who their customers are, what they want, and how much they are willing to pay for it? Data. The entertainment industries, must learn to play a little “moneyball.” The bottom line: follow the data.

Blockchain: Empowering Secure Data Sharing
Blockchain: Empowering Secure Data Sharing

Author: Meng Shen

Publisher: Springer Nature

Published: 2020-07-15

Total Pages: 135

ISBN-13: 9811559392

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With the development of big data, data sharing has become increasingly popular and important in optimizing resource allocation and improving information utilization. However, the expansion of data sharing means there is an urgent need to address the issue of the privacy protection – an area where the emerging blockchain technology offers considerable advantages. Although there are a large number of research papers on data sharing modeling and analysis of network security, there are few books dedicated to blockchain-based secure data sharing. Filing this gap in the literature, the book proposes a new data-sharing model based on the blockchain system, which is being increasingly used in medical and credit reporting contexts. It describes in detail various aspects of the model, including its role, transaction structure design, secure multi-party computing and homomorphic encryption services, and incentive mechanisms, and presents corresponding case studies. The book explains the security architecture model and the practice of building data sharing from the blockchain infrastructure, allowing readers to understand the importance of data sharing security based on the blockchain framework, as well as the threats to security and privacy. Further, by presenting specific data sharing case studies, it offers insights into solving data security sharing problems in more practical fields. The book is intended for readers with a basic understanding of the blockchain infrastructure, consensus mechanisms, smart contracts, secure multiparty computing, homomorphic encryption and image retrieval technologies.

Privacy@work
Privacy@work

Author: Frank Hendrickx

Publisher: Kluwer Law International B.V.

Published: 2023-06-12

Total Pages: 668

ISBN-13: 9403531665

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The right to privacy is a fundamental right. Along with the related right to personal data protection, it has come to take a central place in contemporary employment relations and shows significant relevance for the future of work. This thoroughly researched volume, which offers insightful essays by leading European academics and policymakers in labour and employment law, is the first to present a thoroughly up-to-date Europe-wide survey and analysis of the intensive and growing interaction of workplace relations systems with developments in privacy law. With abundant reference to the EU’s General Data Protection Regulation, the case law of the European Court of Human Rights, and the work of the International Labour Organisation, the book proceeds as a series of country chapters, each by a recognised expert in a specific jurisdiction. Legal comparison is based on a questionnaire circulated to the contributors in advance. Each country chapter addresses the national legal weight of such issues and topics as the following: interaction of privacy and data protection law; legitimacy, purpose limitation, and data minimisation; transparency; role of consent; artificial intelligence and automated decision-making; health-related data, including biometrics and psychological testing; monitoring and surveillance; and use of social media. A detailed introductory overview begins the volume. The research for this book is based on a dynamic methodology, founded in scientific desk research and expert networking. Recognising that the need for further guidance for privacy at work has been demonstrated by various European and international bodies, this book delivers a signal contribution to the field for social partners, practitioners, policymakers, scholars, and all other stakeholders working at the crossroads of privacy, data protection, and labour law.

Noise Filtering for Big Data Analytics
Noise Filtering for Big Data Analytics

Author: Souvik Bhattacharyya

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2022-06-21

Total Pages: 195

ISBN-13: 3110697262

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This book explains how to perform data de-noising, in large scale, with a satisfactory level of accuracy. Three main issues are considered. Firstly, how to eliminate the error propagation from one stage to next stages while developing a filtered model. Secondly, how to maintain the positional importance of data whilst purifying it. Finally, preservation of memory in the data is crucial to extract smart data from noisy big data. If, after the application of any form of smoothing or filtering, the memory of the corresponding data changes heavily, then the final data may lose some important information. This may lead to wrong or erroneous conclusions. But, when anticipating any loss of information due to smoothing or filtering, one cannot avoid the process of denoising as on the other hand any kind of analysis of big data in the presence of noise can be misleading. So, the entire process demands very careful execution with efficient and smart models in order to effectively deal with it.

Sharing Economy and Big Data Analytics
Sharing Economy and Big Data Analytics

Author: Soraya Sedkaoui

Publisher: John Wiley & Sons

Published: 2020-01-09

Total Pages: 209

ISBN-13: 111969499X

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The different facets of the sharing economy offer numerous opportunities for businesses ? particularly those that can be distinguished by their creative ideas and their ability to easily connect buyers and senders of goods and services via digital platforms. At the beginning of the growth of this economy, the advanced digital technologies generated billions of bytes of data that constitute what we call Big Data. This book underlines the facilitating role of Big Data analytics, explaining why and how data analysis algorithms can be integrated operationally, in order to extract value and to improve the practices of the sharing economy. It examines the reasons why these new techniques are necessary for businesses of this economy and proposes a series of useful applications that illustrate the use of data in the sharing ecosystem.

Big Data and Knowledge Sharing in Virtual Organizations
Big Data and Knowledge Sharing in Virtual Organizations

Author: Gyamfi, Albert

Publisher: IGI Global

Published: 2019-01-25

Total Pages: 336

ISBN-13: 1522575200

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Knowledge in its pure state is tacit in nature—difficult to formalize and communicate—but can be converted into codified form and shared through both social interactions and the use of IT-based applications and systems. Even though there seems to be considerable synergies between the resulting huge data and the convertible knowledge, there is still a debate on how the increasing amount of data captured by corporations could improve decision making and foster innovation through effective knowledge-sharing practices. Big Data and Knowledge Sharing in Virtual Organizations provides innovative insights into the influence of big data analytics and artificial intelligence and the tools, methods, and techniques for knowledge-sharing processes in virtual organizations. The content within this publication examines cloud computing, machine learning, and knowledge sharing. It is designed for government officials and organizations, policymakers, academicians, researchers, technology developers, and students.