Sensitive Micro Data Disclosures Based on Tuple Grouping Methods

Data anonymization is one key aspect of micro data disclosures as they enable policy-makers to analyze the decision outcomes of issues influencing the business there by influencing the future course of actions. Privacy is a key issue here because inappropriate disclosure of certain data assets will harm the prospects. Prior approaches of data anonymization such as generalization and bucketization (driven by k-anonymity, and l-diversity) have been designed for privacy preserving micro data publishing which have several limitations like generalization's inability to handle high dimensional data.

Provided by: International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE) Topic: Data Management Date Added: Dec 2013 Format: PDF

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