Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

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Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)


Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)


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Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

  • Sales Rank: #2943014 in eBooks
  • Published on: 2010-08-02
  • Released on: 2010-08-02
  • Format: Kindle eBook

About the Author

Benjamin C. M. Fung is an assistant professor in the Concordia Institute for Information Systems Engineering at Concordia University in Montreal, Quebec. Dr. Fung is also a research scientist and the treasurer of the National Cyber-Forensics and Training Alliance Canada (NCFTA Canada).

Ke Wang is a professor in the School of Computing Science at Simon Fraser University in Burnaby, British Columbia.

Ada Wai-Chee Fu is an associate professor in the Department of Computer Science and Engineering at the Chinese University of Hong Kong.

Philip S. Yu is a professor in the Department of Computer Science and the Wexler Chair in Information and Technology at the University of Illinois at Chicago.

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Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) PDF

Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) PDF
Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) PDF

Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)


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