In today’s data-driven world, data outsourcing has grown, increasing the importance of data security and privacy. Data encryption, while... Show moreIn today’s data-driven world, data outsourcing has grown, increasing the importance of data security and privacy. Data encryption, while providing some protection, is insufficient against side-channel attacks such as access pattern leakage. This thesis focuses on designing and optimizing efficient oblivious access methods to enhance data security and privacy. Traditional solutions, like Oblivious RAM (ORAM), often impose significant overheads, limiting their market adoption. Our research proposes novel oblivious data access schemes tailored to specific applications, systems, and contexts. This approach enables us to identify critical vulnerabilities and performance bottlenecks, and balance performance, security, and other relevant parameters.
In this thesis, I present four published works in Chapters 3 to 6, demonstrating the effectiveness of my proposed methods: (1) optimizing Ring ORAM for multi-channel memory systems, (2) introducing a multi-range supported ORAM for locality-aware applications, (3) proposing an oblivious data access solution for NVM hybrid memory systems, and (4) developing an oblivious access method for deep neural networks (DNNs), ensuring privacy without sacrificing performance. These contributions address unique challenges across application domains, enhancing data security and privacy in contemporary computing systems.
This thesis provides a comprehensive investigation of targeted oblivious access methods, highlighting the benefits of the proposed designs, and contributing to more effective solutions for access pattern leakage mitigation, ultimately improving data security and privacy in contemporary computing systems. Show less
