Privacy-preserving computing: for big data analytics and AI
Privacy-preserving computing aims to protect the personal information of users while capitalizing on the possibilities unlocked by big data. This practical introduction for students, researchers, and industry practitioners is the first cohesive and systematic presentation of the field's advance...
Gespeichert in:
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| Format: | E-Book |
| Sprache: | Englisch Chinesisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2024
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| Links: | https://doi.org/10.1017/9781009299534 |
| Zusammenfassung: | Privacy-preserving computing aims to protect the personal information of users while capitalizing on the possibilities unlocked by big data. This practical introduction for students, researchers, and industry practitioners is the first cohesive and systematic presentation of the field's advances over four decades. The book shows how to use privacy-preserving computing in real-world problems in data analytics and AI, and includes applications in statistics, database queries, and machine learning. The book begins by introducing cryptographic techniques such as secret sharing, homomorphic encryption, and oblivious transfer, and then broadens its focus to more widely applicable techniques such as differential privacy, trusted execution environment, and federated learning. The book ends with privacy-preserving computing in practice in areas like finance, online advertising, and healthcare, and finally offers a vision for the future of the field. |
| Umfang: | 1 Online-Ressource (xii, 255 Seiten) |
| ISBN: | 9781009299534 |
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| spelling | Chen, Kai 1977- Privacy-preserving computing. English Privacy-preserving computing for big data analytics and AI Kai Chen, Qiang Yang Cambridge Cambridge University Press 2024 1 Online-Ressource (xii, 255 Seiten) txt c cr Privacy-preserving computing aims to protect the personal information of users while capitalizing on the possibilities unlocked by big data. This practical introduction for students, researchers, and industry practitioners is the first cohesive and systematic presentation of the field's advances over four decades. The book shows how to use privacy-preserving computing in real-world problems in data analytics and AI, and includes applications in statistics, database queries, and machine learning. The book begins by introducing cryptographic techniques such as secret sharing, homomorphic encryption, and oblivious transfer, and then broadens its focus to more widely applicable techniques such as differential privacy, trusted execution environment, and federated learning. The book ends with privacy-preserving computing in practice in areas like finance, online advertising, and healthcare, and finally offers a vision for the future of the field. Yang, Qiang 1961- Erscheint auch als Druck-Ausgabe 9781009299510 |
| spellingShingle | Chen, Kai 1977- Privacy-preserving computing for big data analytics and AI |
| title | Privacy-preserving computing for big data analytics and AI |
| title_alt | Privacy-preserving computing. |
| title_auth | Privacy-preserving computing for big data analytics and AI |
| title_exact_search | Privacy-preserving computing for big data analytics and AI |
| title_full | Privacy-preserving computing for big data analytics and AI Kai Chen, Qiang Yang |
| title_fullStr | Privacy-preserving computing for big data analytics and AI Kai Chen, Qiang Yang |
| title_full_unstemmed | Privacy-preserving computing for big data analytics and AI Kai Chen, Qiang Yang |
| title_short | Privacy-preserving computing |
| title_sort | privacy preserving computing for big data analytics and ai |
| title_sub | for big data analytics and AI |
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