As generalization and bucketization, several anonymization techniques have been designed to publish privacy by preserving microdata. In existing approaches there is a certain amount of information loss through generalization, particularly in high-dimensional data. There is no clear separation between the attributes of quasi-identification and sensitive attributes in case of bucketization. To overcome this problem, we proposed an approach called slicing. The data is divided horizontally and vertically into the cut. Slice provides better disclosure of membership and better data utility than generalization. The slicing is also done in high dimensional data. Slicing preserve efficient algorithm to calculate slice data and protection to the affiliation of disclosure that obey the 'L-diversity requirement. Slices provide better utility than generalization and are more effective than bucketization in high-dimensional data.