Homomorphicencryptiondeeplearning

由 A Stoian 著作 · 2023 · 被引用 8 次 — Abstract:Fully homomorphic encryption (FHE) is an encryption method that allows to perform computation on encrypted data, without decryption.

由 JW Lee 著作 · 2021 · 被引用 158 次 — Abstract:Fully homomorphic encryption (FHE) is one of the prospective tools for privacypreserving machine learning (PPML), and several PPML ...

由 D Kahrobaei 著作 · 2020 · 被引用 124 次 — Encryption techniques such as fully homomorphic encryption (FHE) enable evaluation over encrypted data. Using FHE, machine learning models such as deep learning ...

由 JW Lee 著作 · 2022 · 被引用 158 次 — Abstract: Fully homomorphic encryption (FHE) is a prospective tool for privacy-preserving machine learning (PPML). Several PPML models have ...

2022年3月10日 — FHE allows you to perform computations on encrypted data without first decrypting that data. The resulting computations are left in an encrypted ...

由 K Lauter 著作 · 被引用 1814 次 — A review of homomorphic encryption and software tools for encrypted statistical machine learning. ... Context-dependent pre-trained deep neural networks for large ...

2023年8月7日 — Homomorphic encryption enables exciting new possibilities for privacy in deep learning systems: **Encrypted data:** Sensitive user data like ...