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The main objective of data mining is to extract previously unknown patternsfrom large collection of data. With the rapid growth in hardware, software andnetworking technology there is outstanding growth in the amount datacollection. Organizations collect huge volumes of data from heterogeneousdatabases which also contain sensitive and private information about andindividual .The data mining extracts novel patterns from such data which can beused in various domains for decision making .The problem with data miningoutput is that it also reveals some information, which are considered to beprivate and personal. Easy access to such personal data poses a threat toindividual privacy. There has been growing concern about the chance of misusingpersonal information behind the scene without the knowledge of actual dataowner. Privacy is becoming an increasingly important issue in many data miningapplications in distributed environment. Privacy preserving data miningtechnique gives new direction to solve this problem. PPDM gives valid datamining results without learning the underlying data values .The benefits ofdata mining can be enjoyed, without compromising the privacy of concernedindividuals. The original data is modified or a process is used in such a waythat private data and private knowledge remain private even after the miningprocess. In this paper we have proposed a framework that allows systemictransformation of original data using randomized data perturbation techniqueand the modified data is then submitted as result of client's query throughcryptographic approach. Using this approach we can achieve confidentiality atclient as well as data owner sites. This model gives valid data mining resultsfor analysis purpose but the actual or true data is not revealed.

Preserving Privacy and Sharing the Data in Distributed Environment using Cryptographic Technique on Perturbed data