Privacy-Preserving Data Mining Models and Algorithms /

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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Aggarwal, Charu C. (Επιμελητής έκδοσης), Yu, Philip S. (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Boston, MA : Springer US, 2008.
Σειρά:Advances in Database Systems, 34
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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245 1 0 |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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490 1 |a Advances in Database Systems,  |x 1386-2944 ;  |v 34 
505 0 |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. 
520 |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 0 |a Data encryption (Computer science). 
650 0 |a Database management. 
650 0 |a Data mining. 
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650 2 4 |a Data Mining and Knowledge Discovery. 
650 2 4 |a Data Encryption. 
650 2 4 |a Database Management. 
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650 2 4 |a Information Systems Applications (incl. Internet). 
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700 1 |a Yu, Philip S.  |e editor. 
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