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Importance Of Confidentiality Classification

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Abstract— Confidentiality-preservation is now a major field of interest .In real world, many companies would like to extract the information of the other without actually giving out their own information. This is particularly useful in many applications. In this paper, we aim to advance a worldwide classification model based on the Naïve Bayes classification scheme. The Naïve Bayes classification is chosen because of its applicability in case of its previous history. For Confidentiality-preservation of the data, the concept of counterweight providing reliable party is used.
Keywords- Confidentiality-preservation, Naïve Bayes, distributed databases, partition.

I. INTRODUCTION

In current years, There has been an advancement of computing and …show more content…
The Key method such as secure cooperative calculation use some transformation on the data in order to perform the Confidentiality preservation. . One of the methods in distributed computing environment which uses the secure sum multiparty computation technique of Confidentiality preservation is Naïve Bayes classification.

Many Papers have discussed and addressed this issue . One of important drawback with the existing systems of computation is that the worldwide pattern computation is done at one of the data points itself. This paper addresses this issue effectively by using a trusted alliance who is responsible for computing result for aggregate classification data of the collaborating parties. Secondly all the class types need not be present. This issue reports by providing two counterweights, one for the newly starting class and other for the existing class.

The remaining of the paper is organized as follows: Section II related research work. Section III presents Confidentiality-Conserving Naïve Bayes classification using trusted alliance computation with two points over distributed databases. Section IV gives experimental results. Section V includes …show more content…
The necessary parameters are simply the counts of instances. nac and na. Due to horizontal partitioning of data, each party has partial information about every attribute. Each party can locally compute the local count of instances. The global count is given by the sum of the local counts. Secure computing a global count is straightforward. Assuming that the total number of instances is public, the required probability can be computed by dividing the appropriate global sums where the local number of instances will not be

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