Handbook of Sharing Confidential Data : Differential Privacy, Secure Multiparty Computation, and Synthetic Data (Hardcover)

1. Introduction??Part 1. The Big Picture? ?2. Protecting Confidential Data through Non-Statistical Methods? 3. 21st Century Statistical Disclosure Limitation: Motivations and Challenges??Part 2. Formal Privacy Techniques??4. Review of Popular Algorithms for Differential Privacy? 5. Privacy Implications of Practical Model Design Choices? 6. Query answering for tabular data? 7. Machine learning with differential privacy? 8. Statistical Inference and Differential Privacy? 9. Systems Issues in Formally Private Systems??Part 3. Synthetic Data? ?10. Synthetic Data? 11. Methods for Synthetic Data Generation? 12. Validation Services for Confidential Data??Part 4. Secure Multiparty Computation??13. Privacy-Preserving Distributed Computation? 14. Differential Privacy and Cryptography? 15. Overview of Secure Multi-Party Computation Applications in Health Research and Social Sciences??Part 5. Use Cases? ?16. Differential Privacy Implementations? 17. Synthpop a tool to enable more flexible use of sensitive data within the Scottish Longitudinal Study? 18. Safe Data Technologies: Safely Expanding Access to Administrative Tax Data? 19. Secure Federated Learning: Integrated Statistical Modeling for Healthcare Applications

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