Cargando…
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Springer US : Imprint: Springer,
2008.
|
| Edición: | 1st ed. 2008. |
| Colección: | Advances in Database Systems
|
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-0-387-70992-5 | ||
| 003 | DE-He213 | ||
| 005 | 20220113153338.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 100301s2008 xxu| s |||| 0|eng d | ||
| 020 | |a 9780387709925 |9 978-0-387-70992-5 | ||
| 024 | 7 | |a 10.1007/978-0-387-70992-5 |2 doi | |
| 050 | 4 | |a QA76.9.A25 | |
| 072 | 7 | |a UR |2 bicssc | |
| 072 | 7 | |a UTN |2 bicssc | |
| 072 | 7 | |a COM053000 |2 bisacsh | |
| 072 | 7 | |a UR |2 thema | |
| 072 | 7 | |a UTN |2 thema | |
| 082 | 0 | 4 | |a 005.8 |2 23 |
| 245 | 1 | 0 | |a Privacy-Preserving Data Mining |h [electronic resource] : |b Models and Algorithms / |c edited by Charu C. Aggarwal, Philip S. Yu. |
| 250 | |a 1st ed. 2008. | ||
| 264 | 1 | |a New York, NY : |b Springer US : |b Imprint: Springer, |c 2008. | |
| 300 | |a XXII, 514 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Advances in Database Systems | |
| 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. . | ||
| 650 | 0 | |a Data protection. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Cryptography. | |
| 650 | 0 | |a Data encryption (Computer science). | |
| 650 | 0 | |a Database management. | |
| 650 | 0 | |a Information storage and retrieval systems. | |
| 650 | 0 | |a Application software. | |
| 650 | 1 | 4 | |a Data and Information Security. |
| 650 | 2 | 4 | |a Data Mining and Knowledge Discovery. |
| 650 | 2 | 4 | |a Cryptology. |
| 650 | 2 | 4 | |a Database Management. |
| 650 | 2 | 4 | |a Information Storage and Retrieval. |
| 650 | 2 | 4 | |a Computer and Information Systems Applications. |
| 700 | 1 | |a Aggarwal, Charu C. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Yu, Philip S. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9780387565408 |
| 776 | 0 | 8 | |i Printed edition: |z 9781441943712 |
| 776 | 0 | 8 | |i Printed edition: |z 9780387709918 |
| 830 | 0 | |a Advances in Database Systems | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-0-387-70992-5 |z Texto Completo |
| 912 | |a ZDB-2-SCS | ||
| 912 | |a ZDB-2-SXCS | ||
| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||