Guide to Data Privacy Models, Technologies, Solutions

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[img]https://i.postimg.cc/rwJ6hFVv/5109b3f1-a817-4eb3-b5c7-638b8d8a622b.png[/img]
English | 2022 | ISBN: ‎ 3031128362 | 323 pages | True PDF EPUB | 21.95 MB
Data privacy technologies are essential for implementing information systems with privacy by design.

Privacy technologies clearly are needed for ensuring that data does not lead to disclosure, but also that statistics or even data-driven machine learning models do not lead to disclosure. For example, can a deep-learning model be attacked to discover that sensitive data has been used for its training This accessible textbook presents privacy models, computational definitions of privacy, and methods to implement them. Additionally, the book explains and gives plentiful examples of how to implement-among other models-differential privacy, k-anonymity, and secure multiparty computation.
Topics and features:
This unique textbook/guide contains numerous examples and succinctly and comprehensively gathers the relevant information. As such, it will be ently suitable for undergraduate and graduate students interested in data privacy, as well as professionals wanting a concise overview.
Vicenc Torra is Professor with the Department of Computing Science at Umea University, Umea, Sweden.

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