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UnifOMR: Oblivious Message Retrieval with Near-optimal Concrete Efficiency

Ben Fisch, Zeyu Liu, Eran Tromer, Yunhao Wang

2026Year
1Top-tier citations

Abstract

End-to-end encryption guarantees message confidentiality but does not hide metadata such as communication patterns among senders and recipients, or their identities. Oblivious Message Retrieval (OMR) is a cryptographic protocol that enables servers to assist recipients in retrieving their messages from a database without learning the mapping between messages and recipients, thereby protecting such metadata. This paper investigates two central questions of OMR: (1) What is the precise relationship between OMR and the better-studied primitive of Private Information Retrieval (PIR)? (2) Can OMR schemes achieve concrete efficiency comparable to state-of-the-art PIR protocols? We show that OMR with a property we call strong detection-key-unlinkability is at least as hard as PIR, and that existing OMR constructions already satisfy this property. This PIR-to-OMR reduction has low overhead, suggesting that OMR cannot be made substantially more efficient than PIR. We then present UnifOMR, which achieves 20× to 1080× faster server runtime over the stateof-the-art SophOMR under practical parameter settings. For 2 19 messages of 612 bytes each, UnifOMR completes in only ∼25 seconds with 4 MB of communication, compared to > 1250 seconds and 260 KB for SophOMR. These gains come with two trade-offs: an asymptotically linear digest size (albeit with small constants), and two rounds of interaction between the detector and the client. Furthermore, crucially, UnifOMR uses batch PIR as a black-box component, which in our experiments accounts for 50-92% of the server runtime. Thus, UnifOMR nearly matches the aforementioned lower bound concretely (for databases of 2 16 to 2 23 messages, each with 612 to 3060 bytes), given the status quo of batch PIR. Thus, our results provide both a theoretical foundation for understanding OMR and a practical step toward making it deployable in real-world privacy-preserving systems.

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