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04254nam a22005655i 4500 |
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978-0-387-70992-5 |
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DE-He213 |
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20151204165700.0 |
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100301s2008 xxu| s |||| 0|eng d |
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|a 9780387709925
|9 978-0-387-70992-5
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|a 10.1007/978-0-387-70992-5
|2 doi
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|d GrThAP
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|a QA76.9.A25
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|a UR
|2 bicssc
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|a UTN
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|a COM053000
|2 bisacsh
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|a 005.8
|2 23
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|a Privacy-Preserving Data Mining
|h [electronic resource] :
|b Models and Algorithms /
|c edited by Charu C. Aggarwal, Philip S. Yu.
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|a Boston, MA :
|b Springer US,
|c 2008.
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|a XXII, 514 p.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
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|a text file
|b PDF
|2 rda
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|a Advances in Database Systems,
|x 1386-2944 ;
|v 34
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|a An 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.
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|a 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. .
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650 |
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|a Computer science.
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|a Computer security.
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|a Data encryption (Computer science).
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|a Database management.
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|a Data mining.
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|a Information storage and retrieval.
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|a Computer Science.
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650 |
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|a Systems and Data Security.
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|a Data Mining and Knowledge Discovery.
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|a Data Encryption.
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650 |
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4 |
|a Database Management.
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650 |
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|a Information Storage and Retrieval.
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650 |
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|a Information Systems Applications (incl. Internet).
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700 |
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|a Aggarwal, Charu C.
|e editor.
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|a Yu, Philip S.
|e editor.
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710 |
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|a SpringerLink (Online service)
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773 |
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|t Springer eBooks
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776 |
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|i Printed edition:
|z 9780387709918
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830 |
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|a Advances in Database Systems,
|x 1386-2944 ;
|v 34
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856 |
4 |
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|u http://dx.doi.org/10.1007/978-0-387-70992-5
|z Full Text via HEAL-Link
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912 |
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|a ZDB-2-SCS
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950 |
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|a Computer Science (Springer-11645)
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