A Novel Method for Privacy Preserving Micro Data Publishing Using Slicing

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Provided by: International Journal of Modern Engineering Research (IJMER)
Topic: Data Management
Format: PDF
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.
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