Organizations like hospitals accumulate huge volume of records from different data sources which may also contain private information. Data mining extract fresh pattern from such data which is used in various domains for proficient decision making. The quandary with data mining is that it also reveal some private information which pose a threat to individual privacy. Privacy Preserving Data Mining (PPDM) gives convincing data mining outcome without revealing the original data. The original medical data is personalized in such a way that the hidden data remains private even after the mining process. In this paper we have proposed a novel scheme for the distributed environment that allows absolute alteration of original medical data using normalization method in each site for generating sanitized data. Coordinator uses a new steganographic approach using contours to send the range to which the data is to be mapped to the desired data owners. The proposed scheme is evaluated in two different dimensions namely privacy and security. Misclassification error is used as a measure to evaluate privacy. For security the experimental results are compared with other steganographic techniques, which show the proposed embedding approach enhances the PSNR of the stego image. This model gives realistic data mining results for analysis purpose without revealing the actual data.