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Occupies an important niche in the privacy-preserving data mining field
Survey information included with each chapter is unique in terms of its focus on introducing the different topics more comprehensively
Provides relative understanding of the work of different communities, such as cryptography, statistical disclosure control, data mining working in the privacy field
Comprehensive and current, bringing together different points of view
Key advances in privacy that just appeared in past three years
Advances in hardware technology have increased the capability to store and record personal data about consumers and individuals. This has caused concerns that personal data may be used for a variety of intrusive or malicious purposes.
Privacy Preserving Data Mining: Models and Algorithms proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. These techniques generally fall into the following categories: data modification techniques, cryptographic methods and protocols for data sharing, statistical techniques for disclosure and inference control, query auditing methods, randomization and perturbation-based techniques. This edited volume also contains surveys by distinguished researchers in the privacy field. Each survey includes the key research content as well as future research directions of a particular topic in privacy.
Privacy Preserving Data Mining: Models and Algorithms is designed for researchers, professors, and advanced-level students in computer science. This book is also suitable for practitioners in industry.
Content Level »Research
Keywords »DOM - Information - K-anonymity - algorithms - association rule hiding - classification - cryptographic approaches - data analysis - data mining - distributed priv - personalized privacy - privacy - query auditing - randonization - stream privacy