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- 212 pages
- 8 hours of reading
More about the book
The book covers data privacy in depth with respect to data mining, test data management, synthetic data generation etc. It formalizes principles of data privacy that are essential for good anonymization design based on the data format and discipline. The principles outline best practices and reflect on the conflicting relationship between privacy and utility. From a practice standpoint, it provides practitioners and researchers with a definitive guide to approach anonymization of various data formats, including multidimensional, longitudinal, time-series, transaction, and graph data. In addition to helping CIOs protect confidential data, it also offers a guideline as to how this can be implemented for a wide range of data at the enterprise level.
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Data Privacy, Nataraj Venkataramanan, Ashwin Shriram
- Language
- Released
- 2016
- product-detail.submit-box.info.binding
- (Hardcover),
- Book condition
- Damaged
- Price
- €13.39
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- Title
- Data Privacy
- Subtitle
- Principles and Practice
- Language
- English
- Authors
- Nataraj Venkataramanan, Ashwin Shriram
- Publisher
- Chapman and Hall/CRC
- Released
- 2016
- Format
- Hardcover
- Pages
- 212
- ISBN10
- 1498721044
- ISBN13
- 9781498721042
- Series
- Tags
- Business, Technology & Engineering, Science & Math, Political Science & Politics, Psychological Topics
- Description
- The book covers data privacy in depth with respect to data mining, test data management, synthetic data generation etc. It formalizes principles of data privacy that are essential for good anonymization design based on the data format and discipline. The principles outline best practices and reflect on the conflicting relationship between privacy and utility. From a practice standpoint, it provides practitioners and researchers with a definitive guide to approach anonymization of various data formats, including multidimensional, longitudinal, time-series, transaction, and graph data. In addition to helping CIOs protect confidential data, it also offers a guideline as to how this can be implemented for a wide range of data at the enterprise level.