摘要：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
