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Authors: Ahmet Cumhur Öztürk and Belgin Ergenç Bostanoğlu

Affiliation: Izmir Institute of Technology, Turkey

ISBN: 978-989-758-273-8

Keyword(s): Privacy Preserving Association Rule Mining, Itemset Hiding, Multiple Sensitive Support Thresholds.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Communication, Collaboration and Information Sharing ; Data Reduction and Quality Assessment ; Foundations of Knowledge Discovery in Databases ; Information Extraction ; Information Security ; Knowledge Discovery and Information Retrieval ; Knowledge Management and Information Sharing ; Knowledge-Based Systems ; Symbolic Systems

Abstract: Itemset mining is the challenging step of association rule mining that aims to extract patterns among items from transactional databases. In the case of applying itemset mining on the shared data of organizations, each party needs to hide its sensitive knowledge before extracting global knowledge for mutual benefit. Ensuring the privacy of the sensitive itemsets is not the only challenge in the itemset hiding process, also the distortion given to the non-sensitive knowledge and data should be kept at minimum. Most of the previous works related to itemset hiding allow database owner to assign unique sensitive threshold for each sensitive itemset however itemsets may have different count and utility. In this paper we propose a new heuristic based hiding algorithm which 1) allows database owner to assign multiple sensitive threshold values for sensitive itemsets, 2) hides all user defined sensitive itemsets, 3) uses heuristics that minimizes loss of information and distortion on the shared database. In order to speed up hiding steps we represent the database as Pseudo Graph and perform scan operations on this data structure rather than the actual database. Performance evaluation of our algorithm Pseudo Graph Based Sanitization (PGBS) is conducted on 4 real databases. Distortion given to the non-sensitive itemsets (information loss), distortion given to the shared data (distance) and execution time in comparison to three similar algorithms is measured. Experimental results show that PGBS is competitive in terms of execution time and distortion and achieves reasonable performance in terms of information loss amongst the other algorithms. (More)

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Paper citation in several formats:
Öztürk, A. and Ergenç Bostanoğlu, B. (2017). Itemset Hiding under Multiple Sensitive Support Thresholds.In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS, ISBN 978-989-758-273-8, pages 222-231. DOI: 10.5220/0006501502220231

@conference{kmis17,
author={Ahmet Cumhur Öztürk. and Belgin Ergen\c{C} Bostanoğlu.},
title={Itemset Hiding under Multiple Sensitive Support Thresholds},
booktitle={Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS,},
year={2017},
pages={222-231},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006501502220231},
isbn={978-989-758-273-8},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS,
TI - Itemset Hiding under Multiple Sensitive Support Thresholds
SN - 978-989-758-273-8
AU - Öztürk, A.
AU - Ergenç Bostanoğlu, B.
PY - 2017
SP - 222
EP - 231
DO - 10.5220/0006501502220231

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