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Advances in Database Systems #34: Privacy-Preserving Data Mining: Models and Algorithms

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Advances in Database Systems #34: Privacy-Preserving Data Mining: Models and Algorithms Cover

 

Synopses & Reviews

Publisher Comments:

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.  

Synopsis:

This book proposes a number of techniques to perform data mining tasks in a privacy-preserving way. The survey information included with each chapter is unique in terms of its focus on introducing the different topics more comprehensively.

Synopsis:

Advances in hardware technology have increased the capability to store and record personal data. This has caused concerns that personal data may be abused. This book proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. This edited volume 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. The book is designed for researchers, professors, and advanced-level students in computer science, but is also suitable for practitioners in industry.

Table of Contents

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.- 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.- Index.

Product Details

ISBN:
9780387709918
Author:
Aggarwal, Charu C.
Publisher:
Springer
Editor:
Yu, Philip S.
Author:
Yu, Philip S.
Subject:
Networking/Security
Subject:
Security - General
Subject:
Database Management - Database Mining
Subject:
Security - Cryptography
Subject:
K-anonymity
Subject:
algorithm
Subject:
association rule hiding
Subject:
cryptographic approaches
Subject:
distributed privacy-preserving data mining
Subject:
high dimensional privacy-preserving data mining
Subject:
personalized privacy
Subject:
Privacy.
Subject:
privacy applications
Subject:
privacy-preserving data mining
Subject:
query auditing
Subject:
randonization
Subject:
stream privacy
Subject:
surveys on privacy-preserving data mining
Subject:
theoretical challenges
Subject:
Systems and Data Security
Subject:
Data Mining and Knowledge Discovery
Subject:
Database management
Subject:
Data encryption.
Subject:
Information storage and retrieval.
Subject:
Information Systems Applications (incl.Internet) <P>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 i
Subject:
Security
Subject:
Information Systems Applications (incl.Internet)
Subject:
General-General
Subject:
Information Storage and Retr
Subject:
ieval
Copyright:
Edition Description:
Book
Series:
Advances in Database Systems
Series Volume:
34
Publication Date:
20080707
Binding:
HARDCOVER
Language:
English
Pages:
535
Dimensions:
235 x 155 mm 2040 gr

Related Subjects

Computers and Internet » Computers Reference » General
Computers and Internet » Networking » Security » Cryptography
Computers and Internet » Networking » Security » General
Health and Self-Help » Health and Medicine » Medical Specialties
Science and Mathematics » Biology » Zoology » General

Advances in Database Systems #34: Privacy-Preserving Data Mining: Models and Algorithms New Hardcover
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$242.25 In Stock
Product details 535 pages Springer - English 9780387709918 Reviews:
"Synopsis" by , This book proposes a number of techniques to perform data mining tasks in a privacy-preserving way. The survey information included with each chapter is unique in terms of its focus on introducing the different topics more comprehensively.
"Synopsis" by , Advances in hardware technology have increased the capability to store and record personal data. This has caused concerns that personal data may be abused. This book proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. This edited volume 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. The book is designed for researchers, professors, and advanced-level students in computer science, but is also suitable for practitioners in industry.
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