TY - GEN
T1 - Automatic construction of generalization hierarchies for publishing anonymized data
AU - Ayala-Rivera, Vanessa
AU - Murphy, Liam
AU - Thorpe, Christina
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - Concept hierarchies are widely used in multiple fields to carry out data analysis. In data privacy, they are known as Value Generalization Hierarchies (VGHs), and are used by generalization algorithms to dictate the data anonymization. Thus, their proper specification is critical to obtain anonymized data of good quality. The creation and evaluation of VGHs require expert knowledge and a significant amount of manual effort, making these tasks highly error-prone and time consuming. In this paper we present AIKA, a knowledge-based framework to automatically construct and evaluate VGHs for the anonymization of categorical data. AIKA integrates ontologies to objectively create and evaluate VGHs. It also implements a multi-dimensional reward function to tailor the VGH evaluation to different use cases. Our experiments show that AIKA improved the creation of VGHs by generating VGHs of good quality in less time than when manually done. Results also showed how the reward function properly captures the desired VGH properties.
AB - Concept hierarchies are widely used in multiple fields to carry out data analysis. In data privacy, they are known as Value Generalization Hierarchies (VGHs), and are used by generalization algorithms to dictate the data anonymization. Thus, their proper specification is critical to obtain anonymized data of good quality. The creation and evaluation of VGHs require expert knowledge and a significant amount of manual effort, making these tasks highly error-prone and time consuming. In this paper we present AIKA, a knowledge-based framework to automatically construct and evaluate VGHs for the anonymization of categorical data. AIKA integrates ontologies to objectively create and evaluate VGHs. It also implements a multi-dimensional reward function to tailor the VGH evaluation to different use cases. Our experiments show that AIKA improved the creation of VGHs by generating VGHs of good quality in less time than when manually done. Results also showed how the reward function properly captures the desired VGH properties.
UR - https://www.scopus.com/pages/publications/84992623615
U2 - 10.1007/978-3-319-47650-6_21
DO - 10.1007/978-3-319-47650-6_21
M3 - Conference contribution
AN - SCOPUS:84992623615
SN - 9783319476490
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 262
EP - 274
BT - Knowledge Science, Engineering and Management - 9th International Conference, KSEM 2016, Proceedings
A2 - Lehner, Franz
A2 - Fteimi, Nora
PB - Springer Verlag
T2 - 9th International Conference on Knowledge Science, Engineering and Management, KSEM 2016
Y2 - 5 October 2016 through 7 October 2016
ER -