| Differential Privacy (MIT) |
| Wednesday, 02 April 2025 | |||
This book looks at the use of differential privacy (DP) for protecting personal data by introducing carefully calibrated random numbers, called statistical noise, when the data is used. Google, Apple, and Microsoft have all integrated the technology into their software, and the US Census Bureau used DP to protect data collected in the 2020 census. In this book, Simson Garfinkel presents the underlying ideas of DP, and helps explain why DP is needed in today’s information-rich environment, why it was used as the privacy protection mechanism for the 2020 census, and why it is so controversial in some communities. 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. Author: Simson L. Garfinkel
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