Least lion optimisation algorithm (LLOA) based secret key generation for privacy preserving association rule hiding

Menaga, D.; Revathi, S.

doi:10.1049/iet-ifs.2017.0634

Cited by 30 publications

(12 citation statements)

References 35 publications

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“…In the past, some people have proposed relevance grouping rules, but the proposed rules only have simple relevance to the data, and the relevance is not high enough. Hence, it is essential to improve it and establish a more accurate association rule function [5].…”

Section: Plos Onementioning

confidence: 99%

International trade and finance exploration using network model of computer trade platform

Zhang

Yuan

2021

PLoS ONE

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International trade becomes increasingly frequent with the deepening of economic globalization. In order to ensure the stable and rapid development of international trade and finance, it is particularly crucial to predict the sales trend of foreign trade goods in advance through the network model of computer trade platform. To optimize the accuracy of sales forecasts for foreign trade goods, under the background of "Internet plus foreign trade", the controllable relevance big data mining of foreign trade goods sales, personalized prediction mechanism, intelligent prediction algorithm, improved distributed quantitative and centralized qualitative calculation are taken as the premise to design dynamic prediction model on export sales based on controllable relevance big data of cross border e-commerce (DPMES). Moreover, after the related experiments and comparative discussions, the forecast error ratios from the first quarter to the fourth quarter are 2.3%, 2.1%, 2.4% and 2.4% respectively, which are also within the acceptable range. The experimental results show that the design combines the advantages of openness and extensibility of Internet plus with dynamic prediction of big data, and achieves the wisdom, quantitative and qualitative prediction of the volume of goods sold under the background of "Internet plus foreign trade", which is controlled by the relevant data of foreign trade. The overall performance of this design is stronger than the previous models, has better dynamic evolution and high practical significance, and is of great significance in the development of international trade and finance.

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Section: Plos Onementioning

confidence: 99%

International trade and finance exploration using network model of computer trade platform

Zhang

Yuan

2021

PLoS ONE

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“…A Least Lion Optimization algorithm (LLOA) [10] was introduced to preserve the privacy of association rules. LLOA was comprised of rule mining stage and secret key generation stage for sanitization.…”

Section: G Bhavani S Sivakumarimentioning

confidence: 99%

Improved Cuckoo Optimization Algorithm for Association Rule Hiding

Bhavani¹,

Sivakumari²

2019

IJITEE

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Association Rule Mining (ARM) is a standard data mining practice used to determine interactions hidden in huge sets. Association Rule Hiding (ARH) methods are used to preserve the privacy of data in ARM. ARH process modifies the original database without changing any non-sensitive rules and data. In order to hide the sensitive rules, cuckoo search optimization algorithm that was developed for hiding the sensitive association rules (COA4ARH) was proposed for sensitive rule hiding. In COA4ARH, number of transactions that should be modified to hide the sensitive rules is not considered which may leads to more number of iteration. In this paper, two properties are introduced to select less number of transactions to be modified. It makes the COA4ARH algorithm faster, decreases the number of lost rules and is suitable for variety of datasets. In order to increase the rule hiding capability of COA4ARH, new fitness functions are introduced. The new fitness functions reduce the amount of lost rules and avoid generation of ghost rules which are formed as objectives of COA4ARH algorithm. The multiple objectives in COA4ARH are conflicting with each other. This is known as multi-objective optimization problem. The multi-objective optimization deals with set of non-dominated solutions (Pareto front) for the problem having more than one objective. It is solved by using Crowding Distance (CD) which selects the optimal set of solution for association rule hiding. Thus, the proposed Improved COA4ARH- CD (ICOA4ARH-CD) can be suitable for variety of datasets and effectively hides the sensitive rules with fewer side effects.

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“…However, the diffusion method is responsible only for attaining locally optimal result. Metaheuristic-based approaches (Jadhav and Joshi, 2020; Menaga and Revathi, 2018; Jadhav and Gomathi, 2019), such as genetic algorithm (GA), ant colony optimization (ACO) and particle swarm optimization, are used to offer near-optimal solutions in specific time. In Mirjalili et al (2014), a metaheuristic algorithm, named grey wolf optimizer (GWO) is devised, which is motivated from the behavior of grey wolves.…”

Section: Introductionmentioning

confidence: 99%

An elephant herd grey wolf optimization (EHGWO) algorithm for load balancing in cloud

Arora

Dixit

2020

IJPCC

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Purpose The advancements in the cloud computing has gained the attention of several researchers to provide on-demand network access to users with shared resources. Cloud computing is important a research direction that can provide platforms and softwares to clients using internet. However, handling huge number of tasks in cloud infrastructure is a complicated task. Thus, it needs a load balancing (LB) method for allocating tasks to virtual machines (VMs) without influencing system performance. This paper aims to develop a technique for LB in cloud using optimization algorithms. Design/methodology/approach This paper proposes a hybrid optimization technique, named elephant herding-based grey wolf optimizer (EHGWO), in the cloud computing model for LB by determining the optimal VMs for executing the reallocated tasks. The proposed EHGWO is derived by incorporating elephant herding optimization (EHO) in grey wolf optimizer (GWO) such that the tasks are allocated to the VM by eliminating the tasks from overloaded VM by maintaining the system performance. Here, the load of physical machine (PM), capacity and load of VM is computed for deciding whether the LB has to be done or not. Moreover, two pick factors, namely, task pick factor (TPF) and VM pick factor (VPF), are considered for choosing the tasks for reallocating them from overloaded VM to underloaded VM. The proposed EHGWO decides the task to be allocated in the VM based on the newly derived fitness functions. Findings The minimum load and makespan obtained in the existing methods, constraint measure based LB (CMLB), fractional dragonfly based LB algorithm (FDLA), EHO, GWO and proposed EHGWO for the maximum number of VMs is illustrated. The proposed EHGWO attained minimum makespan with value 814,264 ns and minimum load with value 0.0221, respectively. Meanwhile, the makespan values attained by existing CMLB, FDLA, EHO, GWO, are 318,6896 ns, 230,9140 ns, 1,804,851 ns and 1,073,863 ns, respectively. The minimum load values computed by existing methods, CMLB, FDLA, EHO, GWO, are 0.0587, 0.026, 0.0248 and 0.0234. On the other hand, the proposed EHGWO with minimum load value is 0.0221. Hence, the proposed EHGWO attains maximum performance as compared to the existing technique. Originality/value This paper illustrates the proposed LB algorithm using EHGWO in a cloud computing model using two pitch factors, named TPF and VPF. For initiating LB, the tasks assigned to the overloaded VM are reallocated to under loaded VMs. Here, the proposed LB algorithm adapts capacity and loads for the reallocation. Based on TPF and VPF, the tasks are reallocated from VMs using the proposed EHGWO. The proposed EHGWO is developed by integrating EHO and GWO algorithm using a new fitness function formulated by load of VM, migration cost, load of VM, capacity of VM and makespan. The proposed EHGWO is analyzed based on load and makespan.

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Least lion optimisation algorithm (LLOA) based secret key generation for privacy preserving association rule hiding

Cited by 30 publications

References 35 publications

International trade and finance exploration using network model of computer trade platform

International trade and finance exploration using network model of computer trade platform

Improved Cuckoo Optimization Algorithm for Association Rule Hiding

An elephant herd grey wolf optimization (EHGWO) algorithm for load balancing in cloud

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