Micro-aggregation-based heuristics for p-sensitive k-anonymity: one step beyond

Micro-data protection is a hot topic in the field of Statistical Disclosure Control (SDC), that has gained special interest after the disclosure of 658000 queries by the AOL search engine in August 2006. Many algorithms, methods and properties have been proposed to deal with micro-data disclosure. p-Sensitive k-anonymity has been recently defined as a sophistication of k-anonymity. This new property requires that there be at least p different values for each confidential attribute within the records sharing a combination of key attributes. Like k-anonymity, the algorithm originally proposed to achieve this property was based on generalisations and suppressions; when data sets are numerical this has several data utility problems, namely turning numerical key attributes into categorical, injecting new categories, injecting missing data, and so on. In this article, we recall the foundational concepts of micro-aggregation, k-anonymity and p-sensitive k-anonymity. We show that kanonymity a...

• Type: Conference
• Year: 2008
• Where: EDBT
• Authors: Agusti Solanas, Francesc Sebé, Josep Domingo-Ferrer
• Keywords: Database, EDBT 2008, Numerical Data Sets, Numerical Key Attributes, Statistical Disclosure Control