Using genetic algorithms for attribute grouping in multivariate microaggregation

Using genetic algorithms for attribute grouping in multivariate microaggregation

Anonymization techniques that provide k-anonymity suffer from loss of quality when the data dimensionality is high. Microaggregation techniques are not an exception. Given a set of records, attributes are grouped into non-intersecting subsets and microaggregated independently. While this improves quality by reducing the loss of information, it usually leads to the loss of the k-anonymity proper...

Tftol: Using Genetic Algorithms for Attribute Grouping in Multivariate Microaggregation U Sing Genetic Aigorithms for Attribute Grouping in M Ultivariate Microaggregation

Acknowledgements Foremost, I would like to thank my daily supervisors Victor Muntés and Jordi Nin for their support and guidance during the development of this project. Without their help, this thesis would have not been possible. Also my gratitude to Josep LluÍs Larriba for giving me the opportunity to develop the project at the DAMA-UPC research group. I would also want to thank the people fr...

Beyond Multivariate Microaggregation for Large Record Anonymization

Microaggregation is one of the most commonly employed microdata protection methods. The basic idea of microaggregation is to anonymize data by aggregating original records into small groups of at least k elements and, therefore, preserving k-anonymity. Usually, in order to avoid information loss, when records are large, i.e., the number of attributes of the data set is large, this data set is s...

How to Group Attributes in Multivariate Microaggregation

On the disclosure risk of multivariate microaggregation