在 Hugging Face Endpoints 上運行隱私保護的全同態加密 (FHE) 推理
Original: Running Privacy-Preserving Inferences on Hugging Face Endpoints
This article introduces how to run privacy-preserving inference based on Fully Homomorphic Encryption (FHE) on Hugging Face Endpoints. In…
Hugging Face 與開源密碼學公司 Zama 合作，介紹如何在 Hugging Face Endpoints 上部署全同態加密（FHE）模型。透過 FHE 技術，用戶的敏感數據在傳輸與計算過程中皆保持加密狀態，雲端伺服器可在不解密的情況下完成推理。此方案為醫療、金融等高隱私需求行業提供了一種安全使用雲端 AI 算力的新途徑。
This article introduces how to run privacy-preserving inference based on Fully Homomorphic Encryption (FHE) on Hugging Face Endpoints. In traditional cloud-based AI inference, users must send plaintext data to a server — a significant security and compliance risk when dealing with medical records, financial data, or personal private information. FHE technology allows mathematical computations to be performed directly on encrypted data, meaning that Hugging Face Endpoints can execute model inference without ever knowing the content of the input or being able to learn the output.
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