Academic Journals Database
Disseminating quality controlled scientific knowledge

Customization of Sensitive Association Rules in Data Mining

ADD TO MY LIST
 
Author(s): Manish Vyas

Journal: Journal of Current Engineering Research
ISSN 2250-2637

Volume: 2;
Issue: 6;
Date: 2012;
Original page

Keywords: As large repositories of data contain confidential rules that must be protected before published | association rule hiding becomes one of important privacy preserving data mining problems. This approach gives a new reconstruction-based association rule hid

ABSTRACT
OLAP databases contain confidential rules that must be protected before published, association rule hiding becomes one of important privacy preserving data mining problems. Compared with traditional data modification methods (data distortion and data blocking), data reconstruction is a new promising, but not sufficiently investigated method, which is inspired by the inverse frequent set mining problem. A number of techniques have been cited in the literature provided by various researchers which focus on hiding the sensitive association rules to prevent them to reach the unauthorized segment, but no significant contributions are seen to provide the customized association rules to the unintended segment. My research focuses on proposing a new knowledge sanitization algorithm as well as FP-tree based method for inverse frequent set mining, which can be used in our proposed reconstruction-based framework
Save time & money - Smart Internet Solutions      Why do you need a reservation system?