Abstract: The collection of digital information by governments, corporations, and individuals has created tremendous opportunities for knowledge- and information-based decision making. Driven by mutual benefits, or by regulations that require certain data to be published, there is a demand for the exchange and publication of data among various parties. Data in its original form, however, typically contains sensitive information about individuals, and publishing such data will violate individual privacy. Privacy preserving data mining (PPDM) tends to transform original data, so that sensitive data are preserved. In this research paper we analysis CAMDP (Combination of Additive and Multiplicative Data Perturbation) technique for k-means clustering as a tool for privacy-preserving data mining. We can show that K-Means Clustering algorithm can be efficiently applied to the transformed data and produce exactly the same results as if applied to the original data.