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Title:	A classification method for identifying confidential data to enhance efficiency of query processing over cloud
Authors:	Hussein Ali Fadhil (P78911)
Supervisor:	Rossilawati Sulaiman, Dr.
Keywords:	Data classification Query processing Confidential business information -- Protection
Issue Date:	4-May-2017
Description:	With the increased use of Database-as-a-Service (DAAS), several issues also come in parallel, especially in translating and executing queries to and from the database securely and efficiently. These problems are in response towards potential attacks such as attempting to copy or eavesdrop the database via queries. Existing security mechanisms include securing the queries by using encryption. However, encrypting the queries significantly affects the efficiency of query processing because of the security overhead from the encrypting and decrypting processes. This study aims to address this problem by proposing a divide-and-conquer strategy in which partial encryptions are used in the queries. This is performed by classifying the data into sensitive and non-sensitive categories using a classification approach so that only the sensitive data is being encrypted. The classification used in this study is based on the data classification policy from the Columbia University. Firstly, a manual annotation is conducted to label the data fields into sensitive and non-sensitive categories. Next, rules are generated to classify the queried data. If a query contains sensitive data, the encryption is specifically being applied to the sensitive data, whereas the non-sensitive data is being remained unencrypted. Experiments have been conducted using real-time data from Baghdad University that is related to students’ information consisting 35 tables and 362 fields. The evaluation is based on the comparison of security overhead of the full encryption (without classification) and partial encryption (with the classification) using Advance Encryption Standard (AES). Results showed that the classification method has significantly reduced the time used to process the query. This implies that the partial encryption based on classifying the data into sensitive and non-sensitive categories has improved the efficiency of query processing.,Certification of Master's/Doctoral Thesis" is not available
Pages:	141
Call Number:	TK5105.59.F336 2017 3 tesis
Publisher:	UKM, Bangi
Appears in Collections:	Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat

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