In this modern era of computing, information technology revolution has brought drastic changes in the way data are collected for knowledge mining. The data thus collected are of value when it provides relevant knowledge pertaining to the interest of an organization. Therefore, the real challenge lies in converting high dimensional data into knowledge and to use it for the development of the organization. The data that is collected are generally released on internet for research purposes after hiding sensitive information in it and therefore, privacy preservation becomes the key factor for any organization to safeguard the internal data and also the sensitive information. Much research has been carried out in this regard and one of the earliest is the removal of identifiers. However, the chances of re-identification are very high. Techniques like k-anonymity and l-diversity helps in making the records unidentifiable in their own way, but, these techniques are not fully shielded against attacks. In order to overcome the drawbacks of these techniques, we have proposed improved versions of anonymization algorithms. The result analysis show that the proposed algorithms are better when compared to existing algorithms.