Privacy Preserving Data Using Overlapping Slicing and Attribute Partitioning

Privacy preserving publishing is the kind of techniques to apply privacy to collected vast amount of The data publication processes are today still very difficult. Data often contains personally identifiable information and therefore releasing such data may result in privacy breaches; this is the case for the examples of microdata, e.g., census data and medical data. The proposed techniques in this project accelerate accessing speed of user as well as applying privacy to collected data. Several anonymization techniques were designed for privacy preserving data publishing. Recent work in data publishi information, especially for high dimensional data. Bucketization, on the other hand, does not prevent membership disclosure. I propose an overlapping slicing method for handling high into more than one column, we protect privacy by breaking the association of uncorrelated attributes and preserve data utility by preserving the association between highly correlated attributes. This technique releases mo correlations thereby, overlapping slicing preserves better data utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute.