Fast Algorithms for Hiding Sensitive High-utility Itemsets in Privacy-preserving Utility Mining

Authors

Jerry Chun-Wei Lin, Harbin Institute of TechnologyFollow
Tsu-Yang Wu, Harbin Institute of Technology
Philippe Fournier-Viger, Harbin Institute of TechnologyFollow
Guo Lin, Harbin Institute of Technology
Justin Zhan, University of Nevada, Las VegasFollow
Miroslav Voznak, University of Ostrava

Document Type

Article

Publication Date

1-1-2016

Publication Title

Engineering Applications of Artificial Intelligence

Volume

55

First page number:

269

Last page number:

284

Abstract

High-Utility Itemset Mining (HUIM) is an extension of frequent itemset mining, which discovers itemsets yielding a high profit in transaction databases (HUIs). In recent years, a major issue that has arisen is that data publicly published or shared by organizations may lead to privacy threats since sensitive or confidential information may be uncovered by data mining techniques. To address this issue, techniques for privacy-preserving data mining (PPDM) have been proposed. Recently, privacy-preserving utility mining (PPUM) has become an important topic in PPDM. PPUM is the process of hiding sensitive HUIs (SHUIs) appearing in a database, such that the resulting sanitized database will not reveal these itemsets. In the past, the HHUIF and MSICF algorithms were proposed to hide SHUIs, and are the state-of-the-art approaches for PPUM. In this paper, two novel algorithms, namely Maximum Sensitive Utility-MAximum item Utility (MSU-MAU) and Maximum Sensitive Utility-MInimum item Utility (MSU-MIU), are respectively proposed to minimize the side effects of the sanitization process for hiding SHUIs. The proposed algorithms are designed to efficiently delete SHUIs or decrease their utilities using the concepts of maximum and minimum utility. A projection mechanism is also adopted in the two designed algorithms to speed up the sanitization process. Besides, since the evaluation criteria proposed for PPDM are insufficient and inappropriate for evaluating the sanitization performed by PPUM algorithms, this paper introduces three similarity measures to respectively assess the database structure, database utility and item utility of a sanitized database. These criteria are proposed as a new evaluation standard for PPUM. © 2016 Elsevier Ltd

Keywords

Maximum sensitive utility; Minimum side effects; PPUM; Privacy preserving data mining; Utility mining

Language

English

Repository Citation

Lin, J. C., Wu, T., Fournier-Viger, P., Lin, G., Zhan, J., Voznak, M. (2016). Fast Algorithms for Hiding Sensitive High-utility Itemsets in Privacy-preserving Utility Mining. Engineering Applications of Artificial Intelligence, 55 269-284.
http://dx.doi.org/10.1016/j.engappai.2016.07.003