International Journal of Modern Engineering Research (IJMER)
Data anonymization techniques for privacy-preserving data publishing have received a lot of attention in recent years. Microdata or detailed data contains information about a person, a household or an organization. Most popular anonymization techniques are: Generalization and Bucketization. Generalization transforms the Quasi-Identifiers (QI) in each bucket into \"Less specific but semantically consistent\" values so that tuples in the same bucket cannot be distinguished by their QI- values. In bucketization, one separates the Sensitive Attributes (SAs) from the QIs by randomly permuting the SA values in each bucket. The process of Generalization loses considerable amount of information, especially for high-dimensional data.