Sharing Big Data Safely: Managing Data Security

by Ted Dunning Ellen Friedman

All Indian Reprints of O'Reilly are printed in Grayscale. Many big data-driven companies today are moving to protect certain types of data against intrusion, leaksor 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, developersand data scientists, this book shows you how 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.

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Reviews

[Stefan C · Rating 2 out of 5]

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.

[Bill Metangmo · Rating 3 out of 5]

- 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

[Mike Fowler · Rating 3 out of 5]

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.