Frequent Sets Discovery in Privacy Preserving Quantitative Association Rules Mining

Piotr Andruszkiewicz


This paper deals with discovering frequent sets for quantitative association rules mining with preserved privacy. It focuses on privacy preserving on an individual level, when true individual values, e.g., values of attributes describing customers, are not revealed. Only distorted values and parameters of the distortion procedure are public. However, a miner can discover hidden knowledge, e.g., association rules, from the distorted data. In order to find frequent sets for quantitative association rules mining with preserved privacy, not only does a miner need to discretise continuous attributes, transform them into binary attributes, but also, after both discretisation and binarisation, the calculation of the distortion parameters for new attributes is necessary. Then a miner can apply either MASK (Mining Associations with Secrecy Konstraints) or MMASK (Modified MASK) to find candidates for frequent sets and estimate their supports. In this paper the methodology for calculating distortion parameters of newly created attributes after both discretisation and binarisation of attributes for quantitative association rules mining has been proposed. The new application of MMASK for finding frequent sets in discovering quantitative association rules with preserved privacy has been also presented. The application of MMASK scheme for frequent sets mining in quantitative association rules discovery on real data sets has been experimentally verified. The results of the experiments show that both MASK and MMASK can be applied in frequent sets mining for quantitative association rules with preserved privacy, however, MMASK gives better results in this task.
Author Piotr Andruszkiewicz (FEIT / IN)
Piotr Andruszkiewicz,,
- The Institute of Computer Science
Publication size in sheets0.6
Book Onieva Enrique, Santos Igor, Osaba Eneko , Quintián Héctor, Corchado Emilio (eds.): Hybrid Artificial Intelligent Systems. Proceedings, Lecture Notes In Computer Science, vol. 9121, 2015, Springer International Publishing, ISBN 978-3-319-19643-5, [978-3-319-19644-2], 740 p., DOI:10.1007/978-3-319-19644-2
HAIS_Front_Matter.pdf / 158.81 KB / No licence information
Keywords in EnglishPrivacy preserving data mining – Quantitative association rules – Frequent sets – Discretisation – MMASK
Languageen angielski
qrules.pdf 274.26 KB
Score (nominal)5
Score sourcejournalList
ScoreMinisterial score = 0.0, 03-02-2020, BookChapterSeriesAndMatConfByIndicator
Ministerial score (2013-2016) = 5.0, 03-02-2020, BookChapterSeriesAndMatConfByIndicator
Publication indicators Scopus Citations = 1; WoS Citations = 1
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