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Mondrian multidimensional k-anonymity (2006)
| Content Provider | CiteSeerX |
|---|---|
| Author | Lefevre, Kristen Dewitt, David J. Ramakrishnan, Raghu |
| Description | K-Anonymity has been proposed as a mechanism for protecting privacy in microdata publishing, and numerous recoding “models ” have been considered for achieving kanonymity. This paper proposes a new multidimensional model, which provides an additional degree of flexibility not seen in previous (single-dimensional) approaches. Often this flexibility leads to higher-quality anonymizations, as measured both by general-purpose metrics and more specific notions of query answerability. Optimal multidimensional anonymization is NP-hard (like previous optimal k-anonymity problems). However, we introduce a simple greedy approximation algorithm, and experimental results show that this greedy algorithm frequently leads to more desirable anonymizations than exhaustive optimal algorithms for two single-dimensional models. 1. |
| File Format | |
| Language | English |
| Publisher Date | 2006-01-01 |
| Access Restriction | Open |
| Subject Keyword | Previous Optimal K-anonymity Problem General-purpose Metric New Multidimensional Model Microdata Publishing Exhaustive Optimal Algorithm Single-dimensional Model Simple Greedy Approximation Algorithm Desirable Anonymizations In ICDE Experimental Result Specific Notion Higher-quality Anonymizations Query Answerability Mondrian Multidimensional K-anonymity Optimal Multidimensional Anonymization Additional Degree |
| Content Type | Text |
| Resource Type | Article |
