Privacy Preserving Association Rule Mining in Vertically Partitioned Databases

Lakshmi, N. V. Muthu; Rani, K. Sandhya

doi:10.5120/4883-7321

Cited by 10 publications

(5 citation statements)

References 24 publications

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“…N. Muthu Lakshmi and K. Sandhya Rani [9] explored the vertically partitioned databases and had proposed a model to identify association rules. It considers "n" number of sites, and calculate the protection imperatives alongside information data miner.…”

Section: Related Workmentioning

confidence: 99%

“…Mining must also incorporate the efficiency in usage of information and users should be benefitted in terms of order of protection. The security and protection schemes used in information mining implements the techniques such as, cryptography, buildup, K-secrecy, Ldiversity, randomization techniques [8,9]. The Privacy Preserving Data Mining strategies secure the information by hiding or concealing some sensitive data with the goal that private data isn't easily accessible.…”

Section: Introductionmentioning

confidence: 99%

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A Robust Privacy Preserving Approach of Outsourced Data by Modified Frequent Web Access Pattern

Jahan¹,

Saxena²

2018

IJCA

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Current scenario of large databases is in point of fact a major issue. Although, the conventional information examination seems to deal the extensive amounts of information. But the data analysts also attempt to analyze the productivity of data. This proposed work is an attempt to resolve the issue of digital information security by finding the highly frequent items in the dataset. Modified Frequent Web Access Pattern algorithm was developed in this work which find patterns in two scans. Technique called as super class substitution will be used here for perturbation of sensitive set of rules. It offers an added advantage of reducing the risk and the utility of database is also increased. Our experiment is carried out on a genuine dataset. The outcomes here, have shown that proposed work has better results over the previous methodologies.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Robust Privacy Preserving Approach of Outsourced Data by Modified Frequent Web Access Pattern

Jahan¹,

Saxena²

2018

IJCA

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“…The structure is to modify a trade off among mystery and efficiency. The use cryptographic techniques constantly have computational costs to keep away from information spillage [4,6].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Data Distribution Through Tree Rules in Frequent Pattern Mining

2018

IJSRCSEIT

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Information sharing among the associations is a general development in a couple of zones like business headway and exhibiting. As bit of the touchy principles that ought to be kept private may be uncovered and such disclosure of delicate examples may impacts the advantages of the association that have the data. Subsequently the standards which are delicate must be secured before sharing the data. In this paper to give secure information sharing delicate guidelines are bothered first which was found by incessant example tree. Here touchy arrangement of principles are bothered by substitution. This kind of substitution diminishes the hazard and increment the utility of the dataset when contrasted with different techniques. Examination is done on certifiable dataset. Results shows that proposed work is better as appear differently in relation to various past strategies on the introduce of evaluation parameters.

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“…When data is distributed among different sites, finding the global association rules is a challenging task as the privacy of the individual site's data is to be preserved. In this paper, a model is proposed to find global association rules by preserving the privacy of individual sites data when the data is partitioned horizontally among n number of sites [4].…”

Section: Introductionmentioning

confidence: 99%

Improving Security and Efficiency in Association Rule Mining using PFP-Growth Algorithm via Transaction Splitting

Fathima¹,

Basha²,

Saravanan³

2016

IJCSE

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Data Mining is a technique which is used to discover hidden information from a large database. Frequent item set mining is also an important fundamental problem in data mining. Nowadays, most of the researchers are used association rule mining to find correlation between items and items sets resourcefully. Security is also important problem in data mining. To this end, we propose a transaction splitting based on PFP-growth algorithm and frequent items should keep as secured with the help of cryptography algorithms. PFP-Growth algorithm is advanced to FP-growth algorithm. It consists of both preprocessing phase and mining phase. In the preprocessing phase, we used smart transaction splitting method to improve the utility and tradeoff. In the mining phase, the transformed database and user specified threshold value helps to estimate the number of support computations, so that we can gradually reduce the amount of noise required and the information loss caused by transaction splitting. Using frequent item, we find the global association rules based on association rule mining. In this paper, cryptography technique (AES-Advanced Encryption Standard algorithm) is used to secure the frequent item set. Trusted party should preserve the privacy of individual data while the data is distributed among different sites.

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Privacy Preserving Association Rule Mining in Vertically Partitioned Databases

Cited by 10 publications

References 24 publications

A Robust Privacy Preserving Approach of Outsourced Data by Modified Frequent Web Access Pattern

A Robust Privacy Preserving Approach of Outsourced Data by Modified Frequent Web Access Pattern

An Adaptive Data Distribution Through Tree Rules in Frequent Pattern Mining

Improving Security and Efficiency in Association Rule Mining using PFP-Growth Algorithm via Transaction Splitting

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