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Full Description
Statistical agencies, research organizations, companies, and other data stewards that seek to share data with the public face a challenging dilemma. They need to protect the privacy and confidentiality of data subjects and their attributes while providing data products that are useful for their intended purposes. In an age when information on data subjects is available from a wide range of data sources, as are the computational resources to obtain that information, this challenge is increasingly difficult. The Handbook of Sharing Confidential Data helps data stewards understand how tools from the data confidentiality literature—specifically, synthetic data, formal privacy, and secure computation—can be used to manage trade-offs in disclosure risk and data usefulness.
Key features:
• Provides overviews of the potential and the limitations of synthetic data, differential privacy, and secure computation
• Offers an accessible review of methods for implementing differential privacy, both from methodological and practical perspectives
• Presents perspectives from both computer science and statistical science for addressing data confidentiality and privacy
• Describes genuine applications of synthetic data, formal privacy, and secure computation to help practitioners implement these approaches
The handbook is accessible to both researchers and practitioners who work with confidential data. It requires familiarity with basic concepts from probability and data analysis.
Contents
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
