Memory access patterns have been leveraged to break cryptosystems and exfiltrate data from trusted execution environments (TEEs), among other exploits. This category of attacks, termed side channels, abuses a program’s data-dependent execution patterns to infer secret information. Several proposed techniques aim to obfuscate or eliminate data-dependent execution patterns or hinder attackers’ measurements in order to prevent side-channel attacks. Oblivious RAM (ORAM) has emerged as a successful mechanism for privatizing memory accesses in a provably secure manner. This cryptographic technique has been designed to both obfuscate server requests for cloud settings and to conceal memory accesses for TEEs. Existing ORAM schemes have primarily been characterized by their asymptotic complexity. Current analyses either fail to evaluate ORAM performance on real systems or lack detailed CPU metrics needed to identify bottlenecks and improve future implementations.In this paper, we provide the first workload characterization of ORAM for secure enclave applications. We analyze the performance of seven foundational ORAM schemes across ten benchmarks. Using a subset of six synthetic workloads, we study how execution time scales with increased working set size, and whether memory access patterns affect each ORAM’s performance. We then combine these workloads with four additional application benchmarks from VIP-Bench and perform a topdown microarchitectural analysis to identify critical bottlenecks. Our findings provide valuable insights that can guide developers in creating more efficient ORAM implementations and selecting schemes best suited to their applications.

Data-oblivious programs have gained popularity due to their application in security, but are often dismissed because of anticipated performance loss. In order to better understand these performance concerns, this paper details the first performance characterization of data-oblivious programs. We study mechanical data-oblivious transformations applied to twenty workloads from the VIP-Bench benchmark suite and find that, overall, performance overheads vary widely, with a geomean slowdown of 7.4×. This variance can be attributed to whether or not the data-oblivious transformations affect the workload’s asymptotic complexity. Performance overheads are much lower for the fourteen workloads whose complexity is unaffected, at 1.9× geomean. Further, by reducing control hazards, we find that dataoblivious transformations often result in improved per-instruction performance (e.g., better branch and memory performance) and increase the number of instructions the processor can execute in parallel (e.g., IPC). Leveraging lessons from analyzing these overheads, we study four notably slow data-oblivious workloads and show how algorithmic changes can significantly improve performance–achieving an average 86.4× speedup over the mechanically produced baseline programs. While data-oblivious program execution often incurs overheads, the contributions of this paper show that these overheads can be overcome by compiler and algorithmic optimizations, bringing us closer to achieving efficient and widely-used data-oblivious programs.