Utility aware clustering for publishing transactional data
| dc.contributor.author | Bewong, M. | |
| dc.contributor.author | Liu, J. | |
| dc.contributor.author | Liu, L. | |
| dc.contributor.author | Li, J. | |
| dc.contributor.conference | 21st Pacific-Asia conference on knowledge discovery and data mining (23 May 2017 - 26 May 2017 : Jeju, South Korea) | |
| dc.contributor.editor | Kim, J. | |
| dc.contributor.editor | Shim, K. | |
| dc.contributor.editor | Cao, L. | |
| dc.contributor.editor | Lee, J.G. | |
| dc.contributor.editor | Lin, X. | |
| dc.contributor.editor | Moon, Y.S. | |
| dc.date.issued | 2017 | |
| dc.description.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. | |
| dc.identifier.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 | |
| dc.identifier.doi | 10.1007/978-3-319-57529-2_38 | |
| dc.identifier.isbn | 978-3-319-57528-5 | |
| dc.identifier.issn | 0302-9743 | |
| dc.identifier.issn | 1611-3349 | |
| dc.identifier.orcid | Liu, J. [0000-0002-0794-0404] | |
| dc.identifier.uri | https://hdl.handle.net/11541.2/127028 | |
| dc.language.iso | en | |
| dc.publisher | SPRINGER INTERNATIONAL PUBLISHING AG | |
| dc.publisher.place | Switzerland | |
| dc.relation.ispartofseries | Lecture Notes in Artificial Intelligence | |
| dc.rights | Copyright 2017 Springer International Publishing | |
| dc.source.uri | https://doi.org/10.1007/978-3-319-57529-2_38 | |
| dc.subject | utility aware clustering | |
| dc.subject | publishing | |
| dc.subject | transactional data | |
| dc.title | Utility aware clustering for publishing transactional data | |
| dc.type | Conference paper | |
| pubs.publication-status | Published | |
| ror.mmsid | 9916130870401831 |