Privacy-preserving is one of the most important challenges in a computer world, because of the huge amount of sensitive information on the internet. The paper contains several privacy preservation techniques for data publishing in the real world. There are several privacy attacks are associate but among of them mainly two attacks are record linkage and attribute linkage. Many scientists have proposed methods to preserve the privacy of data publishing such as K-anonymity, ℓ-diversity, t-closeness. K-anonymity can prevent the record linkage but unable to protect attribute linkage. ℓ-diversity technique overcomes the drawback of k-anonymity technique but it fail to protect from membership discloser attack. Tcloseness technique prevents to attribute discloser attack but it fail in identity disclosure attack. Its computational complexity is large. In this paper we present the novel technique call slicing which to be implemented with various data set through prevent the privacy preservation for data publishing. The goals of this paper is re-analysis a number of privacy preservation of data mining technique clearly and then study the advantages and disadvantages of this technique.