Automatic Construction of Generalization Hierarchies for Publishing Anonymized Data

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Title: Automatic Construction of Generalization Hierarchies for Publishing Anonymized Data
Authors: Ayala-Rivera, Vanessa
Murphy, Liam, B.E.
Thorpe, Christina
Permanent link: http://hdl.handle.net/10197/8768
Date: 7-Oct-2016
Abstract: 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 timeconsuming. 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.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Springer
Keywords: Generalization hierarchiesAnonymizationData privacyKnowledge-based framework
DOI: 10.1007/978-3-319-47650-6_21
Language: en
Status of Item: Peer reviewed
Is part of: Lehner, F. and Fteimi, N. (eds.) Lecture Notes in Computer Science (LNCS, volume 9983)
Conference Details: International Conference on Knowledge Science, Engineering and Management (KSEM), Passau, Germany, October, 2016
Appears in Collections:Computer Science Research Collection

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