Complementing Privacy and Utility Trade-Off with Self-Organising Maps

dc.cclicenceN/Aen
dc.contributor.authorMohammed, Kabiru
dc.contributor.authorAyesh, Aladdin
dc.contributor.authorBoiten, Eerke Albert
dc.date.acceptance2021-08-09
dc.date.accessioned2021-10-06T09:39:27Z
dc.date.available2021-10-06T09:39:27Z
dc.date.issued2021-08-17
dc.descriptionopen access articleen
dc.description.abstractIn recent years, data-enabled technologies have intensified the rate and scale at which organisations collect and analyse data. Data mining techniques are applied to realise the full potential of large-scale data analysis. These techniques are highly efficient in sifting through big data to extract hidden knowledge and assist evidence-based decisions, offering significant benefits to their adopters. However, this capability is constrained by important legal, ethical and reputational concerns. These concerns arise because they can be exploited to allow inferences to be made on sensitive data, thus posing severe threats to individuals’ privacy. Studies have shown Privacy-Preserving Data Mining (PPDM) can adequately address this privacy risk and permit knowledge extraction in mining processes. Several published works in this area have utilised clustering techniques to enforce anonymisation models on private data, which work by grouping the data into clusters using a quality measure and generalising the data in each group separately to achieve an anonymisation threshold. However, existing approaches do not work well with high-dimensional data, since it is difficult to develop good groupings without incurring excessive information loss. Our work aims to complement this balancing act by optimising utility in PPDMprocesses. To illustrate this, we propose a hybrid approach, that combines self-organising maps with conventional privacy-based clustering algorithms. We demonstrate through experimental evaluation, that results from our approach produce more utility for data mining tasks and outperforms conventional privacy-based clustering algorithms. This approach can significantly enable large-scale analysis of data in a privacy-preserving and trustworthy manner.en
dc.funderNo external funderen
dc.identifier.citationMohammed, K., Ayesh, A., Boiten, E. (2021) Complementing Privacy and Utility Trade-Off with Self-Organising Maps. Cryptography, 5(3), 20.en
dc.identifier.doihttps://doi.org/10.3390/cryptography5030020
dc.identifier.issn2410-387X
dc.identifier.urihttps://dora.dmu.ac.uk/handle/2086/21316
dc.language.isoenen
dc.peerreviewedYesen
dc.publisherMDPIen
dc.researchinstituteCyber Technology Institute (CTI)en
dc.subjectk-anonymityen
dc.subjectclusteringen
dc.subjectself-organising mapen
dc.subjectprivacy preserving data miningen
dc.titleComplementing Privacy and Utility Trade-Off with Self-Organising Mapsen
dc.typeArticleen

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
cryptography-05-00020.pdf
Size:
490.98 KB
Format:
Adobe Portable Document Format
Description:
open access paper
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
4.2 KB
Format:
Item-specific license agreed upon to submission
Description: