Efficient Association Rules Hiding Using Genetic Algorithms

Bux, Naadiya Khuda; Lu, Mingming; Wang, Jianxin; Hussain, Saajid; Aljeroudi, Yazan

doi:10.3390/sym10110576

Cited by 8 publications

(6 citation statements)

References 31 publications

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“…Ant colony-based solutions to conceal the frequent itemsets obtained improvement in the performance in terms of side effects [26]. Genetic encoding scheme proposed in [27] utilizes objective function to estimate the effect on non-sensitive rules and provides recursive computation to reduce the side effects. ABC4ARH [28] rule hiding algorithm, selects sensitive transactions using neighborhood generation mechanism that balances between exploitation and exploration.…”

Section: Related Workmentioning

confidence: 99%

Privacy preserving association rule hiding using border based approach

Suma

Shobha

2021

IJEECS

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<div>Association rule mining is a well-known data mining technique used for extracting hidden correlations between data items in large databases. In the majority of the situations, data mining results contain sensitive information about individuals and publishing such data will violate individual secrecy. The challenge of association rule mining is to preserve the confidentiality of sensitive rules when releasing the database to external parties. The association rule hiding technique conceals the knowledge extracted by the sensitive association rules by modifying the database. In this paper, we introduce a border-based algorithm for hiding sensitive association rules. The main purpose of this approach is to conceal the sensitive rule set while maintaining the utility of the database and association rule mining results at the highest level. The performance of the algorithm in terms of the side effects is demonstrated using experiments conducted on two real datasets. The results show that the information loss is minimized without sacrificing the accuracy. </div>

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Section: Related Workmentioning

confidence: 99%

Privacy preserving association rule hiding using border based approach

Suma

Shobha

2021

IJEECS

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“…It should be noted that the theoretical background of evolutionary methods is yet to be defined. Nonetheless, genetic algorithms have benefited from a proven record of identifying original solutions to classical problems [41,42]. With respect to regression and VAR estimation, we must mention the recent work of References [32,35].…”

Section: Genetic Varmentioning

confidence: 99%

“…Likelihood of the model parameters given the data [52] Higher is better Number of estimated parameters Lower is better Mean of squared errors for forecasting 5% of the dataset Lower is better Akaike information criterion [41] Lower is better Likelihood of the model parameters given the data [42] Lower is better…”

Section: Criterion Source Decisionmentioning

confidence: 99%

Conditional Granger Causality and Genetic Algorithms in VAR Model Selection

Marica

Horobeț

2019

Symmetry

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Overcoming symmetry in combinatorial evolutionary algorithms is a challenge for existing niching methods. This research presents a genetic algorithm designed for the shrinkage of the coefficient matrix in vector autoregression (VAR) models, constructed on two pillars: conditional Granger causality and Lasso regression. Departing from a recent information theory proof that Granger causality and transfer entropy are equivalent, we propose a heuristic method for the identification of true structural dependencies in multivariate economic time series. Through rigorous testing, both empirically and through simulations, the present paper proves that genetic algorithms initialized with classical solutions are able to easily break the symmetry of random search and progress towards specific modeling.

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“…The concepts of genetic algorithms are based on Darwin's theory of biological evolution and Mendelian genetics [30][31][32][33]. They combine Darwin's theory of survival of the fittest with random exchange theory to create a statistical model that is applicable to the evolution of natural systems and, more generally, as a universal adaptive search method for solving optimization problems.…”

Section: Elementary Algorithmsmentioning

confidence: 99%

Degree Reduction of S-λ Curves Using a Genetic Simulated Annealing Algorithm

Qin

2018

Symmetry

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The S-λ Curves have become an important research subject in computer aided geometric design (CAGD), which owes to its good geometric properties (such as affine invariance, symmetry, and locality). This paper presents a new method to approximate an S-λ curve of degree n by using an S-λ curve of degree n-1. We transform this degree reduction problem into the function optimization problem first, and then using a new genetic simulated annealing algorithm to determine the global optimal solution of the optimization problem. The method can be used to approximate S-λ curves with fixed or unconstrained endpoints. Examples are given to verify the effectiveness of the presented algorithm; and these numeric examples show that the algorithm is not only easy to implement, but also offers high precision, which makes it valuable in practical applications.

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Efficient Association Rules Hiding Using Genetic Algorithms

Cited by 8 publications

References 31 publications

Privacy preserving association rule hiding using border based approach

Privacy preserving association rule hiding using border based approach

Conditional Granger Causality and Genetic Algorithms in VAR Model Selection

Degree Reduction of S-λ Curves Using a Genetic Simulated Annealing Algorithm

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