- We will send in 10–14 business days.
/images/books/13189832/9783639510478.jpg)
- Author: Hitesh Chhinkaniwala
- Publisher: Scholars' Press
- Year: 2014
- Pages: 120
- ISBN-10: 363951047X
- ISBN-13: 9783639510478
- Format: 15.2 x 22.9 x 0.7 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
Privacy Preserving Data Mining - Issues & Techniques (e-book) (used book) | bookbook.eu
Reviews
Description
Huge volume of data from domain specific applications such as medical, financial, telephone, shopping records and individuals are regularly generated. Sharing of these data is proved to be beneficial for data mining application. Since data mining often involves data that contains personally identifiable information and therefore releasing such data may result in privacy breaches. On one hand such data is an important asset to business decision making by analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information for data analysis. In order to share data while preserving privacy, data owner must come up with a solution which achieves the dual goal of privacy preservation as well as accuracy of data mining task mainly clustering and classification. Existing techniques for privacy preserving data mining is designed for traditional static data sets and are not suitable for data streams. Privacy preserving data stream mining is an emerging research area in the field of privacy aware data mining.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 21d.12:08:13
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Hitesh Chhinkaniwala
- Publisher: Scholars' Press
- Year: 2014
- Pages: 120
- ISBN-10: 363951047X
- ISBN-13: 9783639510478
- Format: 15.2 x 22.9 x 0.7 cm, minkšti viršeliai
- Language: English English
Huge volume of data from domain specific applications such as medical, financial, telephone, shopping records and individuals are regularly generated. Sharing of these data is proved to be beneficial for data mining application. Since data mining often involves data that contains personally identifiable information and therefore releasing such data may result in privacy breaches. On one hand such data is an important asset to business decision making by analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information for data analysis. In order to share data while preserving privacy, data owner must come up with a solution which achieves the dual goal of privacy preservation as well as accuracy of data mining task mainly clustering and classification. Existing techniques for privacy preserving data mining is designed for traditional static data sets and are not suitable for data streams. Privacy preserving data stream mining is an emerging research area in the field of privacy aware data mining.
Reviews