Privacy-Preserving Machine Learning : A use-case-driven approach to building and protecting ML pipelines from privacy and security threats
Gain hands-on experience in data privacy and privacy-preserving machine learning with open-source ML frameworks, while exploring techniques and algorithms to protect sensitive data from privacy breaches Key FeaturesUnderstand machine learning privacy risks and employ machine learning algorithms to s...
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| Auteur principal: | |
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| Autres auteurs: | |
| Format: | Livre numérique |
| Langue: | Anglais |
| Publié: |
Birmingham :
Packt Publishing
2024.
Paris : Cyberlibris |
| Accès en ligne: | Accès Université d'Orléans et IFPM |
| Note: |
Couverture. https://static2.cyberlibris.com/books_upload/136pix/9781800564220.jpg Cyberlibris (ScholarVox) corpus Informatique |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Privacy-Preserving Machine Learning, A use-case-driven approach to building and protecting ML pipelines from privacy and security threats, Srinivasa Rao Aravilli, [Foreword by Sam Hamilton], Birmingham, Packt Publishing, 2024, 1 vol. (402 p.), 978-18-0056-467-1 |