Dissertation/Thesis Abstract

An Approach for Designing Secure and High Performance Cloud Systems
by Gai, Keke, Ph.D., Pace University, 2017, 221; 10635446
Abstract (Summary)

Recent expansions of cloud computing have been growing at a phenomenal rate. Security and privacy issues have become a considerable issue while the applications of big data are growing dramatically fast in cloud computing. However, there exists a contradiction between ensuring a high performance and achieving a high-level security and privacy protection due to the restrictions of the computing resources, based on the findings of the literature review. This study focuses on this contradiction issue and intend to develop an approach of effectuating the cloud system design for a high-level security and privacy protection while acquiring a high performance. The work consists of four research tasks that support the solution to the proposed problem. They are (i) designing a Optimal Fully Homomorphic Encryption (O-FHE) mechanism that can both avoid noise and execute efficiently; (ii) designing a privacy-preserving data encryption strategy while considering efficiency; (iii) developing an approach of the data analytics manager system for in-memory big data analytics; (iv) designing an adaptive energy-aware data allocation approach for heterogeneous memory and creating an efficient data allocation approach for cloud-based heterogeneous memory. The research implements experimental evaluations to examine the performance of the proposed approaches. The main contributions of this study address three aspects. First, this study has proposed an O-FHE method that is different from all approaches proposed by the prior researches. Second, this study addresses the contradiction between the data security and system performance and presents a privacy-preserving strategy for secure data transmissions in cloud systems. Finally, this study attempts to increase the computation efficiency by enhancing the functioning of hardware, more specifically, using heterogeneous memory and in-memory data analytics.

Indexing (document details)
Advisor: Qiu, Meikang
Commitee: Chen, Li-Chiou, Zhang, Tao
School: Pace University
Department: Computer Science and Information Technology
School Location: United States -- New York
Source: DAI-B 79/02(E), Dissertation Abstracts International
Subjects: Computer science
Keywords: Cloud computing, Cybersecurity, Fully homomorphic encryption, Heterogeneous memory, In-memory analytics, Privacy-preserving
Publication Number: 10635446
ISBN: 9780355418736
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