Differential Privacy

A robust yet accessible introduction to the idea, history, and key applications of differential privacy—the gold standard of algorithmic privacy protection.


Differential privacy (DP) is an increasingly popular, though controversial, approach to protecting personal data. DP protects confidential data by introducing carefully calibrated random numbers, called
When DP is used to protect confidential data, like an advertising profile based on the web pages you have viewed with a web browser, the noise makes it impossible for someone to take that profile and reverse engineer, with absolute certainty, the underlying confidential data on which the profile was computed. The book also chronicles the history of DP and describes the key participants and its limitations. Along the way, it also presents a short history of the US Census and other approaches for data protection such as de-identification and k-anonymity.

List of contents

Contents
Series Foreword
Preface
Introduction
1 Concepts and Theories
2 Differential Privacy Issues
3 Future Directions
Acknowledgments
Glossary
Notes
Bibliography
Further Reading
List of Figures
Index
Author Bio

About the author

Simson L. Garfinkel researches and writes at the intersection of AI, privacy, and digital forensics. He is a fellow of the AAAS, the ACM, and the IEEE.

Product details

• Authors: Simson L Garfinkel, Simson L. Garfinkel, Garfinkel Simson L.
• Publisher: The MIT Press
• Languages: English
• Product format: Paperback / Softback
• Released: 25.03.2025
• EAN: 9780262551656
• ISBN: 978-0-262-55165-6
• No. of pages: 256
• Dimensions: 127 mm x 178 mm x 16 mm
• Series: The MIT Press Essential Knowledge series
• Subjects: Education and learning > Teaching preparation > Vocational needs; Natural sciences, medicine, IT, technology > IT, data processing > General, dictionaries; COMPUTERS / Security / General, TECHNOLOGY & ENGINEERING / History, Computer security, Information technology: general issues, POLITICAL SCIENCE / Privacy & Surveillance