Protecting privacy through homomorphic encryption:
This book summarizes recent inventions, provides guidelines and recommendations, and demonstrates many practical applications of homomorphic encryption. This collection of papers represents the combined wisdom of the community of leading experts on homomorphic encryption. In the past 3 years, a glob...
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham, Switzerland
Springer Nature
2022
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| Ausgabe: | corrected publication |
| Schlagworte: | |
| Zusammenfassung: | This book summarizes recent inventions, provides guidelines and recommendations, and demonstrates many practical applications of homomorphic encryption. This collection of papers represents the combined wisdom of the community of leading experts on homomorphic encryption. In the past 3 years, a global community consisting of researchers in academia, industry, and government, has been working closely to standardize homomorphic encryption. This is the first publication of whitepapers created by these experts that comprehensively describes the scientific inventions, presents a concrete security analysis, and broadly discusses applicable use scenarios and markets. This book also features a collection of privacy-preserving machine learning applications powered by homomorphic encryption designed by groups of top graduate students worldwide at the Private AI Bootcamp hosted by Microsoft Research. The volume aims to connect non-expert readers with this important new cryptographic technology in an accessible and actionable way. Readers who have heard good things about homomorphic encryption but are not familiar with the details will find this book full of inspiration. Readers who have preconceived biases based on out-of-date knowledge will see the recent progress made by industrial and academic pioneers on optimizing and standardizing this technology. A clear picture of how homomorphic encryption works, how to use it to solve real-world problems, and how to efficiently strengthen privacy protection, will naturally become clear. |
| Beschreibung: | xvi, 176, C1 Seiten Illustrationen |
| ISBN: | 9783030772864 |
Internformat
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| 520 | 3 | |a This book summarizes recent inventions, provides guidelines and recommendations, and demonstrates many practical applications of homomorphic encryption. This collection of papers represents the combined wisdom of the community of leading experts on homomorphic encryption. In the past 3 years, a global community consisting of researchers in academia, industry, and government, has been working closely to standardize homomorphic encryption. This is the first publication of whitepapers created by these experts that comprehensively describes the scientific inventions, presents a concrete security analysis, and broadly discusses applicable use scenarios and markets. This book also features a collection of privacy-preserving machine learning applications powered by homomorphic encryption designed by groups of top graduate students worldwide at the Private AI Bootcamp hosted by Microsoft Research. The volume aims to connect non-expert readers with this important new cryptographic technology in an accessible and actionable way. Readers who have heard good things about homomorphic encryption but are not familiar with the details will find this book full of inspiration. Readers who have preconceived biases based on out-of-date knowledge will see the recent progress made by industrial and academic pioneers on optimizing and standardizing this technology. A clear picture of how homomorphic encryption works, how to use it to solve real-world problems, and how to efficiently strengthen privacy protection, will naturally become clear. | |
| 653 | 0 | |a Computer science—Mathematics. | |
| 653 | 0 | |a Cryptography. | |
| 653 | 0 | |a Data encryption (Computer science). | |
| 653 | 0 | |a Number theory. | |
| 653 | 0 | |a Algebraic geometry. | |
| 653 | 0 | |a Data protection—Law and legislation. | |
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Datensatz im Suchindex
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| author2 | Lauter, Kristin E. 1969- Dai, Wei Laine, Kim |
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| building | Verbundindex |
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| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | corrected publication |
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| illustrated | Illustrated |
| index_date | 2024-07-03T22:18:11Z |
| indexdate | 2024-07-10T09:53:28Z |
| institution | BVB |
| isbn | 9783030772864 |
| language | English |
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| physical | xvi, 176, C1 Seiten Illustrationen |
| publishDate | 2022 |
| publishDateSearch | 2022 |
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| publisher | Springer Nature |
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| spelling | Protecting privacy through homomorphic encryption Kristin Lauter, Wei Dai, Kim Laine (editors) corrected publication Cham, Switzerland Springer Nature 2022 xvi, 176, C1 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier This book summarizes recent inventions, provides guidelines and recommendations, and demonstrates many practical applications of homomorphic encryption. This collection of papers represents the combined wisdom of the community of leading experts on homomorphic encryption. In the past 3 years, a global community consisting of researchers in academia, industry, and government, has been working closely to standardize homomorphic encryption. This is the first publication of whitepapers created by these experts that comprehensively describes the scientific inventions, presents a concrete security analysis, and broadly discusses applicable use scenarios and markets. This book also features a collection of privacy-preserving machine learning applications powered by homomorphic encryption designed by groups of top graduate students worldwide at the Private AI Bootcamp hosted by Microsoft Research. The volume aims to connect non-expert readers with this important new cryptographic technology in an accessible and actionable way. Readers who have heard good things about homomorphic encryption but are not familiar with the details will find this book full of inspiration. Readers who have preconceived biases based on out-of-date knowledge will see the recent progress made by industrial and academic pioneers on optimizing and standardizing this technology. A clear picture of how homomorphic encryption works, how to use it to solve real-world problems, and how to efficiently strengthen privacy protection, will naturally become clear. Computer science—Mathematics. Cryptography. Data encryption (Computer science). Number theory. Algebraic geometry. Data protection—Law and legislation. Security systems. Lauter, Kristin E. 1969- (DE-588)137142757 edt Dai, Wei edt Laine, Kim edt 9783030772871 Erscheint auch als Online-Ausgabe 9783030772871 |
| spellingShingle | Protecting privacy through homomorphic encryption |
| title | Protecting privacy through homomorphic encryption |
| title_auth | Protecting privacy through homomorphic encryption |
| title_exact_search | Protecting privacy through homomorphic encryption |
| title_exact_search_txtP | Protecting privacy through homomorphic encryption |
| title_full | Protecting privacy through homomorphic encryption Kristin Lauter, Wei Dai, Kim Laine (editors) |
| title_fullStr | Protecting privacy through homomorphic encryption Kristin Lauter, Wei Dai, Kim Laine (editors) |
| title_full_unstemmed | Protecting privacy through homomorphic encryption Kristin Lauter, Wei Dai, Kim Laine (editors) |
| title_short | Protecting privacy through homomorphic encryption |
| title_sort | protecting privacy through homomorphic encryption |
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