Private LLM Inference with Homomorphic Encryption
Summary: Tutorial on end-to-end private LLM inference via Fully Homomorphic Encryption, connecting FHE fundamentals with transformer architectures and encrypted execution techniques. Highlights open systems and algorithmic challenges for practical, scalable confidential inference. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Lawrence Lim (University of California Santa Barbara)
- 2. Divyakant Agrawal (University of California Santa Barbara)
- 3. Amr El Abbadi (University of California Santa Barbara)
BibTeX Citation
@article{lim_vldb26,
title = {{Private LLM Inference with Homomorphic Encryption}},
author = {Lim, Lawrence and Agrawal, Divyakant and Abbadi, Amr El},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4927--4931},
doi = {10.14778/3827998.3828152},
url = {https://doi.org/10.14778/3827998.3828152},
year = {2026}
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