Privacy Preserving Data Mining (Advances in Information Security) ePub download
by Christopher W. Clifton,Yu Michael Zhu,Jaideep Vaidya
- ISBN: 1441938478
- ISBN13: 978-1441938473
- ePub: 1224 kb | FB2: 1935 kb
- Language: English
- Category: Networking & Cloud Computing
- Publisher: Springer; Softcover reprint of hardcover 1st ed. 2006 edition (November 19, 2010)
- Pages: 122
- Rating: 4.9/5
- Votes: 767
- Format: lrf lrf mobi rtf
Privacy Preserving Data Mining Advances in Information Security (Том 19).
This volume is also suitable for graduate-level students in computer science. Privacy Preserving Data Mining Advances in Information Security (Том 19).
Электронная книга "Privacy Preserving Data Mining", Jaideep Vaidya, Christopher W. Clifton, Yu Michael Zhu. Эту книгу можно прочитать в Google Play Книгах на компьютере, а также на устройствах Android и iOS. Выделяйте текст, добавляйте закл. Выделяйте текст, добавляйте закладки и делайте заметки, скачав книгу "Privacy Preserving Data Mining" для чтения в офлайн-режиме.
ADVANCES IN INFORMATION SECURITY aims to publish thorough and . Christopher W. Clifton Purdue University Dept. PRIVACY PRESERVING DATA MINING by Jaideep Vaidya, Chris Clifton, Michael Zhu.
ADVANCES IN INFORMATION SECURITY aims to publish thorough and cohesive overviews of specific topics in information security, as well as works that are larger in scope or that contain more detailed background information than can be accommodated in shorter survey articles. The series also serves as a forum for topics that may not have reached a level of maturity to warrant a comprehensive textbook treatment. of Computer Science 250 N. University St. West Lafayette IN 47907-2066.
Privacy Preserving Data Mining Vaidya Jaideep, Clifton Chris, Zhu Michael Springer 9780387258867 : Data mining has emerged as a significant technology for gaining knowledge from vast quantitie. Дата издания: 2006 Серия: Advances in Information Security Язык: ENG Иллюстрации: 20 black & white illustrations, 10 black & white tables, biography Размер: 2. 8 x 1. 6 x . 0 cm Читательская аудитория: Professional & vocational Рейтинг: Поставляется из: Германии Описание: Data mining has emerged as a significant technology for gaining knowledge from vast quantities of data.
Yu Michael Zhu. Clifton. Data mining has emerged as a significant technology for gaining knowledge from vast quantities of data. However, concerns are growing that use of this technology can violate individual privacy. Reference: Differentially Private Outlier Detection in a Collaborative Environment.
Christopher W. Tags: Computers (1), Statistics (1), Security (1), Numerics (1), Data Mining (1). Manufacturer: Springer Release date: 19 December 2005 ISBN-10 : 0387258868 ISBN-13: 9780387258867. add. Separate tags with commas, spaces are allowed. Use tags to describe a product . for a movie Themes heist, drugs, kidnapping, coming of age Genre drama, parody, sci-fi, comedy Locations paris, submarine, new york.
Yu Michael Zhu, Jaideep Vaidya, Christopher W.
book by Jaideep Vaidya. Yu Michael Zhu, Jaideep Vaidya, Christopher W.
Existing techniques for privacy-preserving data mining are cryptography based techniques and perturbation based . Distributed data is very important in modern information driven applications.
Existing techniques for privacy-preserving data mining are cryptography based techniques and perturbation based technique. Cryptographic techniques are too slow for large scale data sets. Perturbation based technique doesn’t give a much privacy for the data which are distributed. Random Decision Tree framework can used for privacy preserving data mining. Most of the applications are using distributed databases because of the availability of data in different databases. Use of distributed data is very challenging because of the difficulty of merging the data which are more private.
Advances in Information Security. x 1. x 2. cm. Users who liked this book, also liked.
Privacy-preserving data mining. Privacy preserving data mining. In Proceedings of the 2000 ACM SIGMOD Conference on Management of Data, Dallas, TX, May 14-19 2000. Privacy-preserving distributed mining of association rules on horizontally partitioned data. Y. Lindell and B. Pinkas. In Advances in Cryptology – CRYPTO 2000, pages 36–54. Springer-Verlag, Aug. 20-24 2000.
Privacy preserving data mining implies the "mining" of knowledge from distributed data without violating the privacy of the individual/corporations involved in contributing the data. This volume provides a comprehensive overview of available approaches, techniques and open problems in privacy preserving data mining. Crystallizing much of the underlying foundation, the book aims to inspire further research in this new and growing area.
Privacy Preserving Data Mining is intended to be accessible to industry practitioners and policy makers, to help inform future decision making and legislation, and to serve as a useful technical reference.