Effective sanitization approaches to hide sensitive utility and frequent itemsets

Rajalaxmi, R. R.; Natarajan, A. M.

doi:10.3233/ida-2012-00560

Cited by 15 publications

(7 citation statements)

References 34 publications

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“…Then in Section 4, we propose a new algorithm named HSUFIBL. In this section, by experimental results, we will prove the efficiency of our algorithm and show that it causes lower side effects in comparison to the previous algorithms proposed in [7,12,14]. And the last section is the conclusion.…”

Section: Introductionmentioning

confidence: 74%

“…High utility and frequent itemset hiding algorithm therefore is developed for the sensitive knowledge protection purpose. In 2012, R a j a l a x m i and N a t a r a j a n [14] have proposed two novel algorithms named MSMU and MCRSU. The methodology [14] is to modify the value of data item to reduce both support and utility of the sensitive itemset to less than the minimum support threshold and minimum utility threshold, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…Although the PPUIM algorithms can be applied to the PPUFIM problem, they result in low performance. To solve this issue, R a j a l a x m i and N a t a r a j a n [14] proposed two algorithms named MSMU (Minimum Support and Maximum Utility) and MCRSU (Maximum Conflict Ratio for Support and Utility). They succeeded in hiding all sensitive itemsets but they failed in minimizing the side effects.…”

Section: Introductionmentioning

confidence: 99%

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Hiding Sensitive High Utility and Frequent Itemsets Based on Constrained Intersection Lattice

Hai

Long

et al. 2022

Cybernetics and Information Technologies

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Hiding high utility and frequent itemset is the method used to preserve sensitive knowledge from being revealed by pattern mining process. Its goal is to remove sensitive high utility and frequent itemsets from a database before sharing it for data mining purposes while minimizing the side effects. The current methods succeed in the hiding goal but they cause high side effects. This paper proposes a novel algorithm, named HSUFIBL, that applies a heuristic for finding victim item based on the constrained intersection lattice theory. This algorithm specifies exactly the condition that allows the application of utility reduction or support reduction method, the victim item, and the victim transaction for the hiding process so that the process needs the fewest data modifications and gives the lowest number of lost non-sensitive itemsets. The experimental results indicate that the HSUFIBL algorithm achieves better performance than previous works in minimizing the side effect.

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Section: Introductionmentioning

confidence: 74%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hiding Sensitive High Utility and Frequent Itemsets Based on Constrained Intersection Lattice

Hai

Long

et al. 2022

Cybernetics and Information Technologies

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“…To address the problem of PPUM, Lin et al [20] proposed another three criteria for performance evaluation namely database structure similarity (DSS), database utility similarity (DUS), and itemset utility similarity (IUS). Rajalaxmi and Natarajan also proposed a similar concept namely utility difference [55] with DUS. Details of the related definitions are shown as follows.…”

Section: E Evaluation Criteria For Ppummentioning

confidence: 99%

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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“…Integer linear programming is adopted in the designed algorithm to obtain a lower ratio of side effects produced in the hiding process. Rajalaxmi and Natarajan [37] proposed two approaches named MSMU and MCRSU to hide the sensitive frequent and utility itemsets. Both algorithms conceal the itemsets until their support and utility fall below the given thresholds, respectively.…”

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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Effective sanitization approaches to hide sensitive utility and frequent itemsets

Cited by 15 publications

References 34 publications

Hiding Sensitive High Utility and Frequent Itemsets Based on Constrained Intersection Lattice

Hiding Sensitive High Utility and Frequent Itemsets Based on Constrained Intersection Lattice

Privacy Preserving Utility Mining: A Survey

An Improved Sanitization Algorithm in Privacy-Preserving Utility Mining

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