Privacy during Data Mining

The large amounts of data stored in computer files are increasing at a very remarkable rate. It is analysed and evaluated that the amount of data in the world is increasing like water coming into the ocean. At the same time, the users or a person who operates these data are expecting more sophisticated Knowledge. The Languages like Structure Query Languages are not adequate to support this increasing demand for information. Data mining makes an effort to solve the problem. We proposed a new approach for maintaining privacy during mining the knowledge from the data stores. Our approach is based on vector quantization, it quantizes the data to its nearest neighbour values that uses two algorithms one LBG and Modified LBG in codebook generation process.