Advancement in the field of Information technology has resulted in tremendous growth in data collection and extraction of unknown patters from this huge pool of data which has become the primary objective of any data mining algorithm. Besides being so effective, some sensitive information is also revealed by mining algorithm. The extracted knowledge is highly confidential and it needs refinement before giving to data mining researchers and the public in order to concentrate on privacy concerns. The data mining techniques have been developed by the researchers to be applied on data bases without violating the privacy of individuals. Over the last decade, many techniques for privacy preserving data mining have come up. In this paper we propose a new approach to preserve sensitive information using fuzzy logic. Clustering is done on the original data set, and then we add noise to the numeric data using a fuzzy membership function that results in distorted data. Set of Clusters generated using the fuzzified data is also equivalent to the original cluster as well as privacy is also achieved. It is also proved that the processing time of the data is considerably reduced when compared to the other methods that are being used for this purpose.