EFFICIENT TECHNIQUES FOR PRESERVING MICRODATA USING SLICING?

Privacy preserving publishing is the kind of techniques to apply privacy to collected vast amount of data. One of the recent problem prevailing is in the field of data publication. The data often consist of personally identifiable information so releasing such data consists of privacy problem. Several anonymization techniques such as generalization and bucketization have been designed for privacy preserving and microdata publishing. But the problem is that generalization loses considerable amount of information and bucketization does not prevent membership disclosure. So we proposed a novel technique called slicing, which partitions the data both vertically and horizontally. An efficient algorithm is also developed for performing slicing that obeys l-diversity requirement. An advantage of slicing is that it can handle high dimensional data. Our experiment confirms that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attributes.