一句话:提出两极放松/完整 ORAM 层次,并用元数据管理与确定性调度降低 ORAM 带宽开销。
云中不可信操作者可观察内存总线地址,即使数据加密也会泄露隐私;ORAM 旨在消除该泄漏,但开销很大。本文提出类似缓存层次的 Relaxed Hierarchical ORAM(ρ):用容量与深度更小、约束更松的轻量 ORAM 前置于完整 ORAM,并针对效率而非容量优化参数;引入新型元数据管理减少放松 ORAM 访问带宽,同时保持不可区分性并加入完整性验证;为消除 LLC 与放松 ORAM 命中率造成的信息泄漏,采用确定性内存调度策略。在内存密集型应用上,最佳 ρ 模型相对 Freecursive ORAM 基线性能提升 50%。
Applications in the cloud are vulnerable to several attack scenarios. In one possibility, an untrusted cloud operator can examine addresses on the memory bus and use this information leak to violate privacy guarantees, even if data is encrypted. The Oblivious RAM (ORAM) construct was introduced to eliminate such information leak and these frameworks have seen many innovations in recent years. In spite of these innovations, the overhead associated with ORAM is very significant. This paper takes a step forward in reducing ORAM memory bandwidth overheads. We make the case that, similar to a cache hierarchy, a lightweight ORAM that fronts the full-fledged ORAM provides a boost in efficiency. The lightweight ORAM has a smaller capacity and smaller depth, and it can relax some of the many constraints imposed on the full-fledged ORAM. This yields a 2-level hierarchy with a relaxed ORAM and a full ORAM. The relaxed ORAM adopts design parameters that are optimized for efficiency and not capacity. We introduce a novel metadata management technique to further reduce the bandwidth for relaxed ORAM access. Relaxed ORAM accesses preserve the indistinguishability property and are equipped with an integrity verification system. Finally, to eliminate information leakage through LLC and relaxed ORAM hit rates, we introduce a deterministic memory scheduling policy. On a suite of memory-intensive applications, we show that the best Relaxed Hierarchical ORAM (ρ) model yields a performance improvement of 50%, relative to a Freecursive ORAM baseline.
DOI 原文 ·
@inproceedings{paperbot4935,
title = {ρ},
author = {Chandrasekhar Nagarajan and Ali Shafiee and Rajeev Balasubramonian and Mohit Tiwari},
booktitle = {ASPLOS},
year = {2019},
doi = {10.1145/3297858.3304045}
}