Book description
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.
Publisher resources
Table of contents
- Preface
- 1. So Secure It’s Lost
- 2. The Challenge: Sharing Data Safely
- 3. Data on a Need-to-Know Basis
- 4. Fake Data Gives Real Answers
- 5. Fixing a Broken Large-Scale Query
- 6. Fraud Detection
-
7. A Detailed Look at log-synth
- Goals
- Maintaining Simplicity: The Role of JSON in log-synth
- Structure
- Sampling Complex Values
- Structuring and De-structuring Samplers
- Extending log-synth
- Using log-synth with Apache Drill
- Choice of Data Generators
- R is for Random
- Benchmark Systems
- Probabilistic Programming
- Differential Privacy Preserving Systems
- Future Directions for log-synth
- 8. Sharing Data Safely: Practical Lessons
- A. Additional Resources
Product information
- Title: Sharing Big Data Safely
- Author(s):
- Release date: September 2015
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781491952122
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