Hiding personalised anonymity of attributes using privacy preserving data mining Online publication date: Wed, 16-Dec-2015
by N. Rajesh; A. Arul Lawrence Selvakumar
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 7, No. 3/4, 2015
Abstract: Privacy preserving data mining (PPDM) is a new direction in the area of data mining, where privacy preserving techniques have been applied to maintain the data privacy. Example through the process of data mining the sensitive data of an individual can be inferred as well as personal information and patterns from non-sensitive data. PPDM (Rajesh and Selvakumar, 2014) based on enumeration and concatenation of attributes using k-anonymity where, the original data is combined using only two attributes to show encrypted one quasi-identifier. So, we proposed a new approach called hiding personalised anonymity for enumerating and concatenating of attributes using PPDM for combination of three attributes to show encrypted one quasi-identifier. We can reconstruct the attributes using encrypted attribute. In this work, we proposed PPDM for combination of three attributes and two level encrypting methods in order to protect the more secure personal information for avoiding sensitive issues using unlimited records.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Advanced Intelligence Paradigms (IJAIP):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook