Compass: Encrypted Semantic Search with High Accuracy

Jinhao Zhu, UC Berkeley; Liana Patel, Stanford University; Matei Zaharia and Raluca Ada Popa, UC Berkeley

We present Compass, a semantic search system for encrypted data that achieves high accuracy, matching state-of-the-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.

OSDI '25 Open Access Sponsored by
King Abdullah University of Science and Technology (KAUST)

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BibTeX
@inproceedings {308796,
author = {Jinhao Zhu and Liana Patel and Matei Zaharia and Raluca Ada Popa},
title = {Compass: Encrypted Semantic Search with High Accuracy},
booktitle = {19th USENIX Symposium on Operating Systems Design and Implementation (OSDI 25)},
year = {2025},
isbn = {978-1-939133-47-2},
address = {Boston, MA},
pages = {915--938},
url = {https://www.usenix.org/conference/osdi25/presentation/zhu-jinhao},
publisher = {USENIX Association},
month = jul
}

Presentation Video