Privacy preserving data publishing approach provides some methods and tools for publishing useful information while preserving data privacy. Anonymization techniques are used for privacy preserving data publishing such as generalization and bucketization techniques are implemented. Generalization loses considerable amount of information and correlations between different attributes are lost. On the other hand bucketization does not provide membership disclosure protection and will not have a distinct separation between quasi-identifying characteristics and hyper sensitive attributes. In this paper, all of the people’s present any novel technique called slicing, which partitions the data both horizontally and vertically, within each bucket values in each column are randomly permutated to break the linking between different columns.