International Journal of Engineering Technology, Management and Applied Sciences (IJETMAS)
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. In this paper, the authors present a novel technique called slicing, which partitions the data both horizontally and vertically.