Privacy-Preserving Machine Learning : A Use-Case-driven Approach to Building and Protecting ML Pipelines from Privacy and Security Threats

Privacy-Preserving Machine Learning A Use-Case-driven Approach to Building and Protecting ML Pipelines from Privacy and Security Threats

Privacy regulations are evolving each year and compliance with privacy regulations is mandatory for every enterprise. Machine learning engineers are required to not only analyze large amounts of data to gain crucial insights, but also comply with privacy regulations to protect sensitive data. This m...

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Bibliographic Details
Main Author: Aravilli, Srinivas Rao (-)
Other Authors: Hamilton, Sam
Format: eBook
Language:Inglés
Published: Birmingham : Packt Publishing, Limited 2023.
Edition:1st ed
Subjects:
Machine learning > Security measures.
Computer networks > Security measures.
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009825857606719

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