International Journal of Scientific Development and Research - IJSDR
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Issue: November 2021

Volume 6 | Issue 11

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Paper Title: Improvising and Securing Encrypted Relational Data using k-Nearest Neighbor Classification
Authors Name: Punam Pratap Jogdand
Unique Id: IJSDR1711025
Published In: Volume 2 Issue 11, November-2017
Abstract: Data mining is diverse in areas such as banking, medicine, scientific research, and government agencies. Classification is a widely used task in data mining. In the past decade, due to increasing privacy concerns, many theoretical and practical classification solutions have been offered under different security models. However, with the recent popularity of cloud computing, users now have the opportunity to outsource their data in encrypted forms, including data mining to the cloud. Because the data in the cloud is encrypted, it is not possible to use a privacy classification technique to maintain personal information. In this paper, we focus on solving encoded data classification problems. In particular, we offer a safe k-NN classification to encrypt data in the cloud. The proposed model protects confidential information, user privacy, and data access hiding. For our understanding, our work was the first to develop a secure k-NN encoder for encrypted data under semi-accurate format. We also analyze the performance of the model we offer using real data sets in various parameter settings.
Keywords: Security, k-NN classifier, cloud databases, encryption
Cite Article: "Improvising and Securing Encrypted Relational Data using k-Nearest Neighbor Classification", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.2, Issue 11, page no.137 - 143, November-2017, Available :http://www.ijsdr.org/papers/IJSDR1711025.pdf
Downloads: 00060118
Publication Details: Published Paper ID: IJSDR1711025
Registration ID:170855
Published In: Volume 2 Issue 11, November-2017
DOI (Digital Object Identifier):
Page No: 137 - 143
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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