A Novel Approach to Improve the Performance of Divisive Clustering-BST

Praveen, P.; Rama, B.

doi:10.1007/978-981-10-3223-3_53

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…Along these lines, our work is orthogonal to Share mind and other puzzle sharing based arrangements. 2.1 Privacy-Preserving Data Mining Agrawal and Srikant [10], Lindell and Pinkas [11] were the first to exhibit the prospect of security protecting under data mining applications. The current PPDM techniques can broadly be orchestrated into two arrangements: (i) data disturbance and (ii) data dissemination.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

“…In like manner, they don't convey correct data mining happens as a result of the development of accurate hullabaloos to the data. On the other hand, Lindell and Pinkas [11] proposed the essential decision tree classifier under the two party setting tolerating the data were dispersed between them. Starting now and into the foreseeable future much work has been dispersed using SMC strategies (e.g., [12]).…”

Section: Related Work and Backgroundmentioning

confidence: 99%

“…Regardless, Stage 2 in PPkNN is totally new. Finally, our observational examinations in Section 6 rely on upon a veritable dataset while the results in [11] rely on upon a reproduced dataset. In addition, new trial results are joined into this paper.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

“…2. 4 Paillier Cryptosystem The Paillier cryptosystem is an extra substance homomorphic similarly, probabilistic open key encryption organize whose security depends on upon the Decisional Composite Residuosity Assumption [11]. Allow Epk to be as far as possible with open key pk given by (N, g), where N is a delayed consequence of two gigantic primes of comparable piece length and g is a generator in Z* N2 .…”

Section: Related Work and Backgroundmentioning

confidence: 99%

“…Also, cloud can in like way get enduring and sensitive data about the true blue data things by viewing the data availability styles paying little personality to the way that the data are encoded [4], [5]. Accordingly, the confirmation/security inconspicuous components of the DMED issue on a cloud are triple: (11) solace of the encoded data, (12) solace of a client's question history, and (13) covering data openness arranges. Current work on security saving information mining (PPDM) (either inconvenience or ensured multi-party estimation (SMC) focused method) can't modify the DMED issue.…”

Section: Introductionmentioning

confidence: 99%

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Data Privacy in Encrypted Relational Data Using K-Nn Clasification

Sheshikala¹

2017

ijarcs

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Data Mining has wide applications in various domains, for instance, keeping cash, arrangement, intelligent research and among government workplaces. Portrayal is one of the usually used assignments in data mining applications. As far back as decade, due to the climb of various assurance issues, various theoretical and convenient responses for the request issue have been proposed under different security models. In any case, with the present pervasiveness of circulated processing, customers now have the opportunity to outsource their data, fit as a fiddle, and moreover the data mining endeavors to the cloud. Since the data on the cloud is in encoded outline, existing security protecting plan procedures are not significant. In this paper, we focus on dealing with the gathering issue over encoded data. In particular, we propose a secured k-NN classifier over mixed data in the cloud. The proposed tradition guarantees the characterization of data, security of customer's information question, and covers the data get to outlines. To the best of our knowledge, our work is the first to develop an ensured kNN classifier over encoded data under the semi-authentic model. In like manner, we observationally inspect the profitability of our proposed tradition using a bona fide dataset under different parameter settings.

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Section: Related Work and Backgroundmentioning

confidence: 99%

Section: Related Work and Backgroundmentioning

confidence: 99%

Section: Related Work and Backgroundmentioning

confidence: 99%

Section: Related Work and Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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