It is often highly valuable for organizations to have their data analyzed by external agents. Data mining is a technique to analyze and extract useful information from large data sets. In the era of information society, sharing and publishing data has been a common practice for their wealth of opportunities. However, the process of data collection and data distribution may lead to disclosure of their privacy. Privacy is necessary to conceal private information before it is shared, exchanged or published. The privacy-preserving data mining (PPDM) has thus has received a significant amount of attention in the research literature in the recent years. Various methods have been proposed to achieve the expected goal. In this paper we have given a brief discussion on different dimensions of classification of privacy preservation techniques. We have also discussed different privacy preservation techniques and their advantages and disadvantages. We also discuss some of the popular data mining algorithms like association rule mining, clustering, decision tree, Bayesian network etc. used to privacy preservation technique.. We also presented few related works in this field.