000			04238nam a22005415i 4500
001			978-0-387-70992-5
003			DE-He213
005			20250710084011.0
007			cr nn 008mamaa
008			100301s2008 xxu| s |||| 0|eng d
020			_a9780387709925 _a99780387709925
024	7		_a10.1007/978-0-387-70992-5 _2doi
082	0	4	_a005.8 _223
100	1		_aAggarwal, Charu C. _eeditor.
245	1	0	_aPrivacy-Preserving Data Mining _h[recurso electrónico] : _bModels and Algorithms / _cedited by Charu C. Aggarwal, Philip S. Yu.
264		1	_aBoston, MA : _bSpringer US, _c2008.
300			_aXXII, 513 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aAdvances in Database Systems, _x1386-2944 ; _v34
505	0		_aAn Introduction to Privacy-Preserving Data Mining -- A General Survey of Privacy-Preserving Data Mining Models and Algorithms -- A Survey of Inference Control Methods for Privacy-Preserving Data Mining -- Measures of Anonymity -- k-Anonymous Data Mining: A Survey -- A Survey of Randomization Methods for Privacy-Preserving Data Mining -- A Survey of Multiplicative Perturbation for Privacy-Preserving Data Mining -- A Survey of Quantification of Privacy Preserving Data Mining Algorithms -- A Survey of Utility-based Privacy-Preserving Data Transformation Methods -- Mining Association Rules under Privacy Constraints -- A Survey of Association Rule Hiding Methods for Privacy -- A Survey of Statistical Approaches to Preserving Confidentiality of Contingency Table Entries -- A Survey of Privacy-Preserving Methods Across Horizontally Partitioned Data -- A Survey of Privacy-Preserving Methods Across Vertically Partitioned Data -- A Survey of Attack Techniques on Privacy-Preserving Data Perturbation Methods -- Private Data Analysis via Output Perturbation -- A Survey of Query Auditing Techniques for Data Privacy -- Privacy and the Dimensionality Curse -- Personalized Privacy Preservation -- Privacy-Preserving Data Stream Classification.
520			_aAdvances 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.
650		0	_aCOMPUTER SCIENCE.
650		0	_aDATA PROTECTION.
650		0	_aDATA ENCRYPTION (COMPUTER SCIENCE).
650		0	_aDATABASE MANAGEMENT.
650		0	_aDATA MINING.
650		0	_aINFORMATION STORAGE AND RETRIEVAL SYSTEMS.
650		0	_aINFORMATION SYSTEMS.
650	1	4	_aCOMPUTER SCIENCE.
650	2	4	_aSYSTEMS AND DATA SECURITY.
650	2	4	_aDATA MINING AND KNOWLEDGE DISCOVERY.
650	2	4	_aDATABASE MANAGEMENT.
650	2	4	_aDATA ENCRYPTION.
650	2	4	_aINFORMATION STORAGE AND RETRIEVAL.
650	2	4	_aINFORMATION SYSTEMS APPLICATIONS (INCL.INTERNET).
700	1		_aYu, Philip S. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387709918
830		0	_aAdvances in Database Systems, _x1386-2944 ; _v34
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-70992-5 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SCS
942			_2ddc _cER
999			_c58194 _d58194