Oblivious Data Structures

Xiao Shaun Wang, Kartik Nayak, Chang Liu, T.-H. Hubert Chan, Elaine Shi, Emil Stefanov, Yan Huang

Abstract: We design novel, asymptotically more efficient data structures and algorithms for programs whose data access patterns exhibit some degree of predictability. To this end, we propose two novel techniques, a pointer-based technique and a locality-based technique. We show that these two techniques are powerful building blocks in making data structures and algorithms oblivious. Specifically, we apply these techniques to a broad range of commonly used data structures, including maps, sets, priority-queues, stacks, deques; and algorithms, including a memory allocator algorithm, max-flow on graphs with low doubling dimension, and shortest-path distance queries on weighted planar graphs. Our oblivious counterparts of the above outperform the best known ORAM scheme both asymptotically and in practice.