Protection of privacy from unauthorized access is one of the primary concerns in datause, from national security to business transactions. It brings out a new branch of data mining,known as Privacy Preserving Data Mining (PPDM). Privacy-Preserving is a major concern in theapplication of data mining techniques to datasets containing personal, sensitive, or confidentialinformation. Data distortion is a critical component to preserve privacy in security-related datamining applications; we propose a QR Decomposition method for data distortion. We focusprimarily on privacy preserving data clustering. As the distorted data occupies small amount ofstorage space, the memory requirement becomes low. Finally, we evaluate the effectiveness ofthe method in terms of misclassification error rate. Our experiments on several data sets revealthat the classification error rate varies as a result of security. However, the method has much lesscomputational cost, especially when new data items are inserted dynamically.