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Characterizing Generalization Algorithms-First Guidelines for Data Publishers

Feten Ben Fredj 1 Nadira Lammari 1 Isabelle Comyn-Wattiau 1
1 CEDRIC - ISID - CEDRIC. Ingénierie des Systèmes d'Information et de Décision
CEDRIC - Centre d'études et de recherche en informatique et communications
Abstract : Many techniques, such as generalization algorithms have been proposed to ensure data anonymization before publishing. However, data publishers may feel unable to choose the best algorithm given their specific context. In this position paper, we describe synthetically the main generalization algorithms focusing on their constraints and their advantages. Then we discuss the main criteria that can be used to choose the best algorithm given a context. Two use cases are proposed, illustrating guidelines to help data holders choosing an algorithm. Thus we contribute to knowledge management in the field of anonymization algorithms. The approach can be applied to select an algorithm among other anonymization techniques (micro-aggregation, swapping, etc.) and even first to select a technique
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Submitted on : Friday, March 6, 2015 - 12:01:31 PM
Last modification on : Wednesday, February 12, 2020 - 3:02:05 PM


  • HAL Id : hal-01126561, version 1



Feten Ben Fredj, Nadira Lammari, Isabelle Comyn-Wattiau. Characterizing Generalization Algorithms-First Guidelines for Data Publishers. KMIS 2014- International Conference on Knowledge Management and Information Sharing, Oct 2014, Rome, Italy. ⟨hal-01126561⟩



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