MOSAIC: Masked Outsourcing of Secure AI Computations
We address the challenge of securely and efficiently outsourcing AI computations from a trusted but computationally weak client to an untrusted but powerful server, in the setting where the client holds both the input and the model, and the server must learn neither. We present MOSAIC, whose core is a novel matrix-multiplication masking protocol that scales to far larger matrices than prior work, enabling the safe outsourcing of modern workloads such as large transformer inference. By introducing small amounts of noise to the multiplication result and thereby relaxing correctness, MOSAIC achieves optimal asymptotic client overhead and concrete runtimes orders of magnitude faster than prio...
| Authors | James Hsin-yu Chiang, Sheila Zingg, Kari Kostiainen et al. |
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| Primary category | cs.CR |
| Published | 31 July 2026 |
| Last updated at source | 25 August 2026 |
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