Sharing Big Data Safely : Managing Data Security

This is not a book to tell you how to build a security system. It’s not about how to lock data down. Instead, we provide solutions for how to share secure data safely. The benefit of collecting large amounts of many different types of data is now widely understood, and it’s increasingly important to keep certain types of data locked down securely in order to protect it against intrusion, leaks, or unauthorized eyes. Big data security techniques are becoming very sophisticated. But how do you keep data secure and yet get access to it when needed, both for people within your organization and for outside experts? The challenge of balancing security with safe sharing of data is the topic of this book. These suggestions for safely sharing data fall into two groups: How to share original data in a controlled way such that each different group using it—such as within your organization—only sees part of the whole dataset. How to employ synthetic data to let you get help from outside experts without ever showing them original data. The book explains in a non-technical way how specific techniques for safe data sharing work. The book also reports on real-world use cases in which customized synthetic data has provided an effective solution. You can read Chapters 1–4 and get a complete sense of the story. In Chapters 5–7, we go on to provide a technical deep-dive into these techniques and use cases and include links to open source code and tips for implementation. 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. "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. Youll 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"--Back cover