Practical and Private Hybrid ML Inference with Fully Homomorphic Encryption
A hybrid approach to fully homomorphic encryption for ML inference improves practicality by selectively applying FHE where privacy is most critical, balancing confidentiality with acceptable latency.
Summary written by editorial AI · Source link below
arXiv:2509.01253v2 Announce Type: replace Abstract: In contemporary cloud-based services, protecting users' sensitive data and ensuring the confidentiality of the server's model are critical. Fully homomorphic encryption (FHE) enables inference directly on encrypted inputs, but its practicality is hindered by expensive bootstrapping and inefficient approximations of non-linear activations. We introduce Safhire, a hybrid inference framework that executes linear layers under encryption on the ser
Editorial Analysis
Practical FHE for ML inference could enable European enterprises to process sensitive data in untrusted cloud environments without exposing plaintext—a key enabler for GDPR-compliant AI services.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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