Optimizing ORAM Datastores for Scalability, Fault Tolerance, and Performance

Abstract

Oblivious RAM (ORAM) mitigates access pattern attacks, where adversaries infer sensitive data by observing access patterns. These attacks can compromise privacy even when data is encrypted. While ORAM ensures privacy by obfuscating these patterns, its adoption in cloud environments faces significant challenges, particularly related to scalability, fault tolerance, and performance. This thesis presents Treebeard: an ORAM-based datastore that addresses these challenges through a novel multi-layer architecture. Unlike traditional ORAM systems that rely on a centralized proxy to manage data access and security, this design separates responsibilities across specialized layers that are independently scalable. Each layer handles distinct functionalities and efficiently batches and processes requests. Treebeard facilitates horizontal scaling, and adds fault tolerance by eliminating single points of failure. Experiments show that Treebeard is scalable, highly performant, and fault-tolerant. Treebeard outperforms existing ORAM systems in terms of throughput while simultaneously addressing scalability and fault tolerance in its design.