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Cloak optimizes Oblivious RAM (ORAM) by exploiting temporal locality in real-world workloads, deviating from the traditional goal of perfectly uniform access patterns. It introduces a "recentness-biased" traffic pattern, prioritizing recent data accesses to maximize the utilization of oblivious traffic. Experiments on Netflix and Ethereum datasets demonstrate that Cloak achieves overheads as low as 1.1x compared to non-oblivious baselines, while maintaining security guarantees.
ORAM can be practical: Cloak achieves near-native performance by cleverly exploiting temporal locality, a common characteristic of real-world workloads.
Encrypted cloud storage can hide data contents but still leak sensitive information through access patterns. ORAM addresses this by hiding access patterns, but existing ORAM systems are too inefficient to deploy in practice. We present Cloak, an oblivious storage system that dramatically improves performance by leveraging a simple, widely observed property of real workloads: temporal locality, where recently accessed items are more likely to be accessed again soon. Instead of trying to make server accesses look perfectly uniform, Cloak makes server traffic follow a fixed,"recentness-biased"pattern and then uses real queries to fill as much of that traffic as possible. When the workload exhibits temporal locality, Cloak achieves overheads as low as $1.1\times$ over a non-oblivious and unencrypted baseline. Importantly, this heuristic affects only performance, not security. We evaluate Cloak on Netflix click-stream and Ethereum transaction traces, achieving 165,000 and 157,000 operations per second, respectively, on a single machine.