Partitioning Based Data Privacy Preservation for Overlapping Slicing Technique

Privacy preservation in data mining provides security to sensitive data in a database against unauthorized access. Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving micro data publishing. Recent work has shown that generalization loses considerable amount of information, especially for high dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. A novel technique called overlapping slicing, which partitions the data both horizontally and vertically and duplicates an attribute in more than one column. overlapping slicing preserves better data utility than generalization and can be used for membership disclosure protection.