Synopsis
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 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.
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Differential Privacy (The MIT Press Essential Knowledge series)
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The MIT Press, 2025
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ISBN 13 : 9780262551656
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Differential Privacy (The MIT Press Essential Knowledge series)
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Differential Privacy (The MIT Press Essential Knowledge series)
Edité par
The MIT Press, 2025
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ISBN 13 : 9780262551656
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Paperback. Etat : New. 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 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. 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. N° de réf. du vendeur LU-9780262551656
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Differential Privacy
Edité par
The MIT Press, 2025
ISBN 10 : 0262551659
ISBN 13 : 9780262551656
Ancien ou d'occasion
Couverture souple
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Paperback. Etat : new. Paperback. A robust yet accessible introduction to the idea, history, and key applications of differential privacy-the gold standard of algorithmic privacy protection.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 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.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. A robust yet accessible introduction to the idea, history, and key applications of differential privacy the gold standard of algorithmic privacy protection. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9780262551656
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