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: Provides
integrated presentation of data privacy (including tools from
statistical disclosure control, privacy-preserving data mining, and
privacy for communications) Discusses privacy requirements and tools
fordifferent types of scenarios, including privacy for data, for
computations, and for users Offers characterization of privacy models,
comparing their differences, advantages, and disadvantages Describes
some of the most relevant algorithms to implement privacy models
Includes examples of data protection mechanisms This unique
textbook/guide contains numerous examples and succinctly and
comprehensively gathers the relevant information. As such, it will be
eminently suitable for undergraduate and graduate students interested
in data privacy, as well as professionals wanting a concise overview.
Vicenç Torra is Professor with the Department of Computing Science
at Umeå University, Umeå, Sweden.
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Produktdetaljer
ISBN
9783031128370
Publisert
2022
Utgiver
Vendor
Springer
Språk
Product language
Engelsk
Format
Product format
Digital bok
Forfatter