Compass: Encrypted Semantic Search with High Accuracy
Jinhao Zhu, Liana Patel, Matei Zaharia, Raluca Ada Popa
摘要
We present Compass, a semantic search system for encrypted data that achieves high accuracy, matching state-ofthe-art plaintext search quality, while ensuring the privacy of data, queries, and results, even if the server is compromised. Compass contributes a novel way to traverse a state-of-the-art graph-based semantic search index and a white-box co-design with Oblivious RAM, a cryptographic primitive that hides access patterns, to enable efficient search over encrypted embeddings. With our techniques, Directional Neighbor Filtering, Speculative Neighbor Prefetch, and Graph-Traversal Tailored ORAM, Compass achieves user-perceived latencies within or around a second and is orders of magnitude faster than baselines under various network conditions.
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引用它的顶会 Paper8
- Enabling Index-free Adjacency in Oblivious Graph Processing with Delayed DuplicationsWeiqi Feng, Xinle Cao, Adam O'Neill, Chuanhui YangVLDB 2026
- Hoss: Fast Oblivious Semantic Search with Heterogeneous GPU-CPU-TEE ArchitectureJianzhang Du, Weijie Huang, Chenghong Wang, Nicolas Tsagareli 等CCS 2026
- SONIC: Concurrent Oblivious RAM & Data Structures for Low-Latency and High-ThroughputNihal Talur, Ioannis DemertzisUSENIX Security 2026
- Accelerating Confidential Databases with Crypto-Free MappingsWenxuan Huang, Zhanbo Wang, Mingyu LiOSDI 2026
- Found in Translation: A Generative Language Modeling Approach to Memory Access Pattern AttacksGrace Jia, Alex Wong, Anurag KhandelwalUSENIX Security 2025
它引用的顶会 Paper27
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- SecureML: A System for Scalable Privacy-Preserving Machine LearningPayman Mohassel, Yupeng ZhangS&P 2017 · 被引用 2,107 次
- Accelerating Large-Scale Inference with Anisotropic Vector QuantizationRuiqi Guo, Philip Sun, Erik Lindgren, Quan Geng 等ICML 2020 · 被引用 539 次
- Generic Attacks on Secure Outsourced DatabasesGeorgios Kellaris, George Kollios, Kobbi Nissim, Adam O'NeillCCS 2016 · 被引用 327 次
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