Abstract
Today as we are living in the era of information explosion, it has
become very important to find out useful information from large
massive data. Also advances in internet, communication and
hardware technology has lead to an increase in the capability of
storing personal data of individuals. Massive amount of data
streams are generated from different applications like medical,
shopping record, network traffic, etc. Sharing such data is very
important asset to business decision making but the fear is that
once the personal data is leaked it can be misused for a variety of
purposes. Hence some amount of privacy preserving needs to be
done on the data before it is released to others. Traditional
methods of privacy preserving data mining (PPDM) are designed
for static data sets which makes it unsuitable for dynamic data
streams. In this paper an efficient and effective data perturbation
method is proposed that aims to protect privacy of sensitive
attribute and obtaining data