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Sharing Big Data Safely: Managing Data Security
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.
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.
96 pages, ebook
Published September 15, 2015
3 people want to read
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Displaying 1 - 3 of 3 reviews
January 10, 2017
I think that the title of this book is misleading. Safely sharing data implies confidentiality, integrity, authenticity etc. Contrary, this book only talks about obfuscating values by using a tool written by the authors. Even in its limited context it fails to explain its most important challenge: how to design KPIs to asses the goodness of fake data.
November 27, 2017
- Share data safely also means provide data outise the company(kaggle) etc ...
- Apache Drill supports views chaining security
- synthetic generated data can be useful to resolve issue if you can't have access to data, stacktraces ... of a client
- Apache Drill supports views chaining security
- synthetic generated data can be useful to resolve issue if you can't have access to data, stacktraces ... of a client
January 29, 2021
Interesting pointers as to why anonymising data is hard. Introduces log-synth, a tool for generating random data that can be used to share data models without the real data.
Displaying 1 - 3 of 3 reviews