Privacy Preserving Data Mining
By: Vaidya, Jaideep [author.].
Contributor(s): Zhu, Yu Michael [author.] | Clifton, Christopher W [author.] | SpringerLink (Online service).
Material type:
BookSeries: Advances in Information Security: 19Publisher: Boston, MA : Springer US, 2006.Description: X, 122 p. 20 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9780387294896.Subject(s): Computer science | Computer communication systems | Data structures (Computer science) | Data encryption (Computer science) | Database management | Data mining | Information storage and retrieval | Computer Science | Data Mining and Knowledge Discovery | Database Management | Data Structures, Cryptology and Information Theory | Data Encryption | Information Storage and Retrieval | Computer Communication NetworksDDC classification: 006.312 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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E books
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PK Kelkar Library, IIT Kanpur | Available | EBK161 |
Privacy and Data Mining -- What is Privacy? -- Solution Approaches / Problems -- Predictive Modeling for Classification -- Predictive Modeling for Regression -- Finding Patterns and Rules (Association Rules) -- Descriptive Modeling (Clustering, Outlier Detection) -- Future Research - Problems remaining.
Data mining has emerged as a significant technology for gaining knowledge from vast quantities of data. However, concerns are growing that use of this technology can violate individual privacy. These concerns have led to a backlash against the technology, for example, a "Data-Mining Moratorium Act" introduced in the U.S. Senate that would have banned all data-mining programs (including research and development) by the U.S. Department of Defense. Privacy Preserving Data Mining provides a comprehensive overview of available approaches, techniques and open problems in privacy preserving data mining. This book demonstrates how these approaches can achieve data mining, while operating within legal and commercial restrictions that forbid release of data. Furthermore, this research crystallizes much of the underlying foundation, and inspires further research in the area. Privacy Preserving Data Mining is designed for a professional audience composed of practitioners and researchers in industry. This volume is also suitable for graduate-level students in computer science.
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