  The main objective of data mining is to extract previously unknown patterns
from large collection of data. With the rapid growth in hardware, software and
networking technology there is outstanding growth in the amount data
collection. Organizations collect huge volumes of data from heterogeneous
databases which also contain sensitive and private information about and
individual .The data mining extracts novel patterns from such data which can be
used in various domains for decision making .The problem with data mining
output is that it also reveals some information, which are considered to be
private and personal. Easy access to such personal data poses a threat to
individual privacy. There has been growing concern about the chance of misusing
personal information behind the scene without the knowledge of actual data
owner. Privacy is becoming an increasingly important issue in many data mining
applications in distributed environment. Privacy preserving data mining
technique gives new direction to solve this problem. PPDM gives valid data
mining results without learning the underlying data values .The benefits of
data mining can be enjoyed, without compromising the privacy of concerned
individuals. The original data is modified or a process is used in such a way
that private data and private knowledge remain private even after the mining
process. In this paper we have proposed a framework that allows systemic
transformation of original data using randomized data perturbation technique
and the modified data is then submitted as result of client's query through
cryptographic approach. Using this approach we can achieve confidentiality at
client as well as data owner sites. This model gives valid data mining results
for analysis purpose but the actual or true data is not revealed.
