Path: Top -> Journal -> Telkomnika -> 2019 -> Vol 17, No 6, Desember 2019

Classifying Confidential Data using SVM for Efficient Cloud Query Processing

Journal from gdlhub / 2020-01-09 15:20:38
Oleh : Huda Kadhim Tayyeh, Ahmed Sabah Al-Jumaili, Telkomnika
Dibuat : 2020-01-09, dengan 1 file

Keyword : advanced standard encryption, cloud database, cloud query processing, support vector machin
Url :
Sumber pengambilan dokumen : web

Nowadays, organizations are widely using a cloud database engine from the cloud service

providers. Privacy still is the main concern for these organizations where every organization is strictly

looking forward more secure environment for their own data. Several studies have proposed different types

of encryption methods to protect the data over the cloud. However, the daily transactions represented by

queries for such databases makes encryption is inefficient solution. Therefore, recent studies presented

a mechanism for classifying the data prior to migrate into the cloud. This would reduce the need of

encryption which enhances the efficiency. Yet, most of the classification methods used in the literature

were based on string-based matching approach. Such approach suffers of the exact match of terms where

the partial matching would not be considered. This paper aims to take the advantage of N-gram

representation along with Support Vector Machine classification. A real-time data will used in

the experiment. After conducting the classification, the Advanced Encryption Standard algorithm will be

used to encrypt the confidential data. Results showed that the proposed method outperformed the baseline

encryption method. This emphasizes the usefulness of using the machine learning techniques for

the process of classifying the data based on confidentiality.

Beri Komentar ?#(0) | Bookmark

PropertiNilai Properti
ID Publishergdlhub
Nama KontakHerti Yani, S.Kom
AlamatJln. Jenderal Sudirman

Print ...


  • , Editor: Calvin