Onyx: Cost-Efficient Disk-Oblivious ANN Search
Approximate nearest neighbor (ANN) search in AI systems increasingly handles sensitive data on third-party infrastructure. Trusted execution environments (TEEs) offer protection, but cost-efficient deployments must rely on external SSDs, which leaks user queries through disk access patterns to the host. Oblivious RAM (ORAM) can hide these access patterns but at a high cost; when paired with existing disk-based ANN search techniques, it makes poor use of SSD resources, yielding high latency and poor cost-efficiency. The core challenge for efficient oblivious ANN search over SSDs is balancing both bandwidth and access count. The state-of-the-art ORAM-ANN design minimizes access count at the...
| Authors | Deevashwer Rathee, Jean-Luc Watson, Zirui Neil Zhao et al. |
|---|---|
| Primary category | cs.CR |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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