EAN: 9783031128363

UNDERGRADUATE TOPICS IN COMPUTER SCIENCE GUID...

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 for different 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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