Guide to Data Privacy
Language: English
Published by Springer International Publishing AG, CH, 2022
Series: Book 85 of 99 - Undergraduate Topics in Computer Science
- Softcover
- New
Seller: Rarewaves.com USA, London, London, United KingdomRarewaves.com USA
AbeBooks seller since June 11, 2025
Condition: New
£ 46.91
Quantity: Over 20 available
Add to basketItem description from seller
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 usersOffers characterization of privacy models, comparing their differences, advantages, and disadvantagesDescribes some of the most relevant algorithms to implement privacy modelsIncludes examples of data protection mechanismsThis 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. …
Seller Inventory # LU-9783031128363
- Title
- Guide to Data Privacy
- Author
- Vicenç Torra
- Publisher
- Springer International Publishing AG, CH
- Publication year
- 2022
- Condition
- New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 3031128362
- ISBN 13
- 9783031128363
- Edition
- 1st ed. 2022.
- Series
- Book 85 of 99: Undergraduate Topics in Computer Science
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.
"Synopsis" may belong to another edition of this title.
About the Author
"About the title" may belong to another edition of this title.
Rarewaves.com USA
London, London, United Kingdom
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