Utility aware clustering for publishing transactional data
Date
2017
Authors
Bewong, M.
Liu, J.
Liu, L.
Li, J.
Editors
Kim, J.
Shim, K.
Cao, L.
Lee, J.G.
Lin, X.
Moon, Y.S.
Shim, K.
Cao, L.
Lee, J.G.
Lin, X.
Moon, Y.S.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017 / Kim, J., Shim, K., Cao, L., Lee, J.G., Lin, X., Moon, Y.S. (ed./s), vol.10235 LNAI, pp.481-494
Statement of Responsibility
Conference Name
21st Pacific-Asia conference on knowledge discovery and data mining (23 May 2017 - 26 May 2017 : Jeju, South Korea)
Abstract
This work aims to maximise the utility of published data for the partition-based anonymisation of transactional data. We make an observation that, by optimising the clustering i.e. horizontal partitioning, the utility of published data can significantly be improved without affecting the privacy guarantees. We present a new clustering method with a specially designed distance function that considers the effect of sensitive terms in the privacy goal as part of the clustering process. In this way, when the clustering minimises the total intra-cluster distances of the partition, the utility loss is also minimised. We present two algorithms DocClust and DetK for clustering transactions and determining the best number of clusters respectively.
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Copyright 2017 Springer International Publishing