Date of Graduation
Doctor of Philosophy in Computer Science (PhD)
Computer Science & Computer Engineering
Second Committee Member
Third Committee Member
Applied sciences, Cloud, Database security, Databases, Encrypted, Partitioning, Queries
Many features and advantages have been brought to organizations and computer users by Cloud computing. It allows different service providers to distribute many applications and services in an economical way. Consequently, many users and companies have begun using cloud computing. However, the users and companies are concerned about their data when data are stored and managed in the Cloud or outsourcing servers. The private data of individual users and companies is stored and managed by the service providers on the Cloud, which offers services on the other side of the Internet in terms of its users, and consequently results in privacy concerns . In this dissertation, a technique has been explored to improve query processing performance while protecting database tables on a Cloud by encrypting those so that they remain secure. It shows how to process SQL queries on encrypted databases designed to protect data from any leakage or attack, even from the service providers. The strategy is to process the query on the Cloud without having to decrypt the data, and data decryption is performed only at the client site. Therefore, to achieve efficiency, no more than the exact set of requested data is returned to the client. In addition, four different techniques have been developed to index and partition the data. The indexes and partitions of the data are used to select part of the data from the Cloud or outsource data depending on the required data. The index data can be stored on the Cloud or server with the encrypted database table. This helps in reducing the entire processing time, which includes data transfer time from the Cloud to the client and also data decryption and processing time at the client.
Omran, O. M. (2016). Data Partitioning Methods to Process Queries on Encrypted Databases on the Cloud. Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/1580