Data Structures and Algorithms


Preserving Privacy in Data Mining using Data Distortion Approach

Authors: Mrs. Prachi Karandikar, Sachin Deshpande

Data mining, the extraction of hidden predictive information from large databases, is nothing but discovering hidden value in the data warehouse. Because of the increasing ability to trace and collect large amount of personal information, privacy preserving in data mining applications has become an important concern. Data distortion is one of the well known techniques for privacy preserving data mining. The objective of these data perturbation techniques is to distort the individual data values while preserving the underlying statistical distribution properties. These techniques are usually assessed in terms of both their privacy parameters as well as its associated utility measure. In this paper, we are studying the use of non-negative matrix factorization (NMF) with sparseness constraints for data distortion.

Comments: 8 Pages.

Download: PDF

Submission history

[v1] 2016-01-09 22:08:59

Unique-IP document downloads: 87 times is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. will not be responsible for any consequences of actions that result from any form of use of any documents on this website.

Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.

comments powered by Disqus