Data Mining is the process of analyzing data from different perspectives, summarizing it, and extracting the needed information from the database. Most enterprises are collecting and storing data in large databases.

Database privacy is an important responsibility of organizations to protect clients sensitive information because client trusts them to do so. Various anonymization techniques have been proposed for the privacy of sensitive microdata.

Generalization loses a considerable amount of information, especially for high-dimensional data. Bucketization does not prevent membership disclosure and does not apply to data that do not have a clear separation between quasi-identifying attributes and sensitive attributes.

Slicing is a technique proposed for anonymized published datasets by partitioning the dataset vertically and horizontally. The proposed technique increases the utility and privacy of a sliced dataset by allowing overlapped slicing while maintaining the prevention of membership disclosure.

It also provides secure data access for multiple domains. This novel approach works on overlapped slicing to improve, preserve data utility and privacy better traditional slicing.

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