MICF: An effective sanitization algorithm for hiding sensitive patterns on data mining

Li, Yu-Chiang; Yeh, Jieh-Shan; Chang, Chin‐Chen

doi:10.1016/j.aei.2006.12.003

Cited by 21 publications

(10 citation statements)

References 22 publications

(31 reference statements)

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“…Aggregate, Hybrid and Disaggregate algorithms presented in [10] perform better than the SWA algorithm [9] in terms of data utility but suffers from computational complexity. Item grouping algorithm (IGA) presented in [8] is improved in [11] to decrease the number of modifications. Hong et al in [12] proposed a greedy SIF-IDF algorithm that uses TF-IDF measure from information retrieval to compute the correspondence between sensitive itemsets and transactions.…”

Section: Related Workmentioning

confidence: 99%

Association rule hiding using integer linear programming

Suma

Shobha

2021

IJECE

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<span>Privacy preserving data mining has become the focus of attention of government statistical agencies and database security research community who are concerned with preventing privacy disclosure during data mining. Repositories of large datasets include sensitive rules that need to be concealed from unauthorized access. Hence, association rule hiding emerged as one of the powerful techniques for hiding sensitive knowledge that exists in data before it is published. In this paper, we present a constraint-based optimization approach for hiding a set of sensitive association rules, using a well-structured integer linear program formulation. The proposed approach reduces the database sanitization problem to an instance of the integer linear programming problem. The solution of the integer linear program determines the transactions that need to be sanitized in order to conceal the sensitive rules while minimizing the impact of sanitization on the non-sensitive rules. We also present a heuristic sanitization algorithm that performs hiding by reducing the support or the confidence of the sensitive rules. The results of the experimental evaluation of the proposed approach on real-life datasets indicate the promising performance of the approach in terms of side effects on the original database.</span>

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Section: Related Workmentioning

confidence: 99%

Association rule hiding using integer linear programming

Suma

Shobha

2021

IJECE

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“…Three side effects in PPDM, including hiding failures (HF), missing cost (MC) and artificial cost (AC) are presented. Most researchers [16], [18], [44], [47], [48], [50], [53] adopt these three side effects to measure the performance of their proposed algorithms. Hiding failure means some information has not been hidden completely after the sanitization process.…”

Section: B Privacy-preserving Utility Miningmentioning

confidence: 99%

“…Since sensitive itemset usually contains more than one item, Li et al proposed the MICF (maximum item conflict first) [47] algorithm to hide sensitive patterns. MICF uses the maximum item which appears in sensitive itemsets as the target item in each step.…”

Section: B Privacy Preserving Utility Miningmentioning

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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“…Data sanitization in the IGA is based on the degree of sensitive transactions. 35 The maximum item conflict first (MICF) 37 is an itemset-based and multiple-ruleshiding algorithm. This algorithm is intended to overcome the overlap problem in the IGA 36 and decrease the number of deleted items.…”

Section: Heuristic Distortion-based Approachesmentioning

confidence: 99%

Comprehensive Survey on Privacy Preserving Association Rule Mining: Models, Approaches, Techniques and Algorithms

Alwatban

Emam

2014

Int. J. Artif. Intell. Tools

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In recent years, a new research area known as privacy preserving data mining (PPDM) has emerged and captured the attention of many researchers interested in preventing the privacy violations that may occur during data mining. In this paper, we provide a review of studies on PPDM in the context of association rules (PPARM). This paper systematically defines the scope of this survey and determines the PPARM models. The problems of each model are formally described, and we discuss the relevant approaches, techniques and algorithms that have been proposed in the literature. A profile of each model and the accompanying algorithms are provided with a comparison of the PPARM models.

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MICF: An effective sanitization algorithm for hiding sensitive patterns on data mining

Cited by 21 publications

References 22 publications

Association rule hiding using integer linear programming

Association rule hiding using integer linear programming

Privacy Preserving Utility Mining: A Survey

Comprehensive Survey on Privacy Preserving Association Rule Mining: Models, Approaches, Techniques and Algorithms

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