An efficient algorithm for Hiding High Utility Sequential Patterns

Le, Bac; Dinh, Duy-Tai; Huynh, Van–Nam; Nguyen, Quang‐Minh; Fournier-Viger, Philippe

doi:10.1016/j.ijar.2018.01.005

Cited by 23 publications

(7 citation statements)

References 9 publications

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“…We plan to extend this approach for mixed numeric and categorical data clustering, as well as parallel methods for clustering large-scale data sets. In addition, we have some ideas to adapt the categorical data clustering framework to the topics of high-utility sequential pattern mining/hiding [19,20,38,39].…”

Section: Discussionmentioning

confidence: 99%

k-PbC: an improved cluster center initialization for categorical data clustering

Dinh

Huynh

2020

Appl Intell

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The performance of a partitional clustering algorithm is influenced by the initial random choice of cluster centers. Different runs of the clustering algorithm on the same data set often yield different results. This paper addresses that challenge by proposing an algorithm named k-PbC, which takes advantage of non-random initialization from the view of pattern mining to improve clustering quality. Specifically, k-PbC first performs a maximal frequent itemset mining approach to find a set of initial clusters. It then uses a kernel-based method to form cluster centers and an information-theoretic based dissimilarity measure to estimate the distance between cluster centers and data objects. An extensive experimental study was performed on various real categorical data sets to draw a comparison between k-PbC and state-of-the-art categorical clustering algorithms in terms of clustering quality. Comparative results have revealed that the proposed initialization method can enhance clustering results and k-PbC outperforms compared algorithms for both internal and external validation metrics.

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Section: Discussionmentioning

confidence: 99%

k-PbC: an improved cluster center initialization for categorical data clustering

Dinh

Huynh

2020

Appl Intell

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“…The HHUSP-A uses the ascending order of utility of HUSP, while the HHUSP-D relies on the descending order to improve the performance of HHUSP by decreasing execution time and missing cost. The main ideas of these four algorithms for hiding HUSPs had been illustrated in [62]. Their general process for hiding HUSP is composed of three steps: 1) Mining step: uses a HUSPM algorithm to mine all HUSPs form the original sequential database; 2) Sorting step: sorts the set of derived HUSPs using a specific order; and 3) Hiding step: modifies the original database and returns the final sanitized database.…”

Section: F Ppum For Sequence Datamentioning

confidence: 99%

“…For example, a HUSP may have a lower, equal or higher utility than any its sub-sequence. In order to hide high-utility sequential patterns (HUSPs) in sequence data, several related algorithms are further developed, such as HHUSP [60], MSPCF [60], HHUSP-A [61], HHUSP-D [61] and HUS-Hiding [62], as shown in Fig. 3.…”

Section: F Ppum For Sequence Datamentioning

confidence: 99%

“…However, despite this improvement, hiding HUSPs remains a very time and memory consuming process. Recently, Le et al presented a novel algorithm named HUS-Hiding [62] which relies on a novel structure to enhance the sanitization process. Experimental results show HUS-Hiding has a better performance than the previous PPUM algorithms for sequence data.…”

Section: F Ppum For Sequence Datamentioning

confidence: 99%

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Privacy Preserving Utility Mining: A Survey

Gan

Chun-Wei

Chao

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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“…us, the approach outperforms the other algorithms in minimizing the side effects. Besides the above works, Le et al [39] proposed an efficient algorithm for hiding high-utility sequential patterns, which relies on a novel structure to enhance the sanitization process.…”

mentioning

confidence: 99%

An Improved Sanitization Algorithm in Privacy-Preserving Utility Mining

Liu

Chen

Wen

et al. 2020

Mathematical Problems in Engineering

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High-utility pattern mining is an effective technique that extracts significant information from varied types of databases. However, the analysis of data with sensitive private information may cause privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, privacy-preserving utility mining (PPUM) has become an important research topic in recent years. The MSICF algorithm is a sanitization algorithm for PPUM. It selects the item based on the conflict count and identifies the victim transaction based on the concept of utility. Although MSICF is effective, the heuristic selection strategy can be improved to obtain a lower ratio of side effects. In our paper, we propose an improved sanitization approach named the Improved Maximum Sensitive Itemsets Conflict First Algorithm (IMSICF) to address this issue. It dynamically calculates conflict counts of sensitive items in the sanitization process. In addition, IMSICF chooses the transaction with the minimum number of nonsensitive itemsets and the maximum utility in a sensitive itemset for modification. Extensive experiments have been conducted on various datasets to evaluate the effectiveness of our proposed algorithm. The results show that IMSICF outperforms other state-of-the-art algorithms in terms of minimizing side effects on nonsensitive information. Moreover, the influence of correlation among itemsets on various sanitization algorithms’ performance is observed.

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An efficient algorithm for Hiding High Utility Sequential Patterns

Cited by 23 publications

References 9 publications

k-PbC: an improved cluster center initialization for categorical data clustering

k-PbC: an improved cluster center initialization for categorical data clustering

Privacy Preserving Utility Mining: A Survey

An Improved Sanitization Algorithm in Privacy-Preserving Utility Mining

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