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001 978-0-387-70992-5
003 DE-He213
005 20161121230712.0
007 cr nn 008mamaa
008 100301s2008 xxu| s |||| 0|eng d
020 _a9780387709925
_9978-0-387-70992-5
024 7 _a10.1007/978-0-387-70992-5
_2doi
050 4 _aQA76.9.A25
072 7 _aUR
_2bicssc
072 7 _aUTN
_2bicssc
072 7 _aCOM053000
_2bisacsh
082 0 4 _a005.8
_223
245 1 0 _aPrivacy-Preserving Data Mining
_h[electronic resource] :
_bModels and Algorithms /
_cedited by Charu C. Aggarwal, Philip S. Yu.
264 1 _aBoston, MA :
_bSpringer US,
_c2008.
300 _aXXII, 514 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_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 _aComputer security.
650 0 _aData encryption (Computer science).
650 0 _aDatabase management.
650 0 _aData mining.
650 0 _aInformation storage and retrieval.
650 1 4 _aComputer Science.
650 2 4 _aSystems and Data Security.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aData Encryption.
650 2 4 _aDatabase Management.
650 2 4 _aInformation Storage and Retrieval.
650 2 4 _aInformation Systems Applications (incl. Internet).
700 1 _aAggarwal, Charu C.
_eeditor.
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
912 _aZDB-2-SCS
950 _aComputer Science (Springer-11645)
999 _c502698
_d502698