mirror of
https://github.com/hanzoai/papers.git
synced 2026-11-03 02:27:30 +00:00
zeekay
0d1d121451
Fix shared/ include paths to ../shared/ for 55 subdirectory papers so
that hanzocover.sty and lstlang.tex resolve when building from each
paper's own directory (the build pattern used by latexmk per paper).
Build fixes:
- hanzo-operative/sections/03-screen-understanding-pipeline.tex: move
\COMMENT from after \FOR{...} onto the \STATE line so the algorithmic
env doesn't mis-parse.
- hanzo-network-architecture (abstract + sections 04, 12, 13): escape
unicode mu (U+03BC) as $\mu$ for pdflatex.
- ntt-transform/sections/08-benchmark-results.tex: same mu escape.
All 55 Tier-2/3 papers now build clean with latexmk -pdf.
78 lines
3.0 KiB
TeX
78 lines
3.0 KiB
TeX
% Hanzo FHE Inference: Fully Homomorphic Encryption for Confidential AI
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\documentclass[11pt]{article}
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\usepackage{../shared/hanzocover}
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\usepackage{amsmath, amssymb, amsthm}
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\usepackage{mathtools}
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\usepackage{bm}
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\usepackage{graphicx}
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\usepackage{booktabs}
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\usepackage{hyperref}
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\usepackage{enumitem}
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\usepackage{algorithm}
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\usepackage{algpseudocode}
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\usepackage{xcolor}
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\usepackage{float}
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\usepackage{multirow}
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\hypersetup{colorlinks=true,linkcolor=black,citecolor=blue,urlcolor=blue}
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\newtheorem{theorem}{Theorem}
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\newtheorem{lemma}[theorem]{Lemma}
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\newtheorem{proposition}[theorem]{Proposition}
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\newtheorem{corollary}[theorem]{Corollary}
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\newtheorem{definition}{Definition}
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\newtheorem{remark}{Remark}
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\title{HANZO FHE INFERENCE\\[0.3em]
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{\large Fully Homomorphic Encryption for\\Confidential AI Inference}}
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\author{
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Zach Kelling \\
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\textit{Hanzo Industries Inc (Techstars '17)} \\
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\texttt{[email protected]}
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}
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\date{March 2026}
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\begin{document}
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\hanzocoverpage
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% ============================================================================
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% ABSTRACT
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% ============================================================================
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\begin{abstract}
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We present Hanzo FHE Inference, a system for running machine learning models on
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encrypted data using Fully Homomorphic Encryption (FHE). The system enables
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privacy-preserving AI-as-a-service: clients encrypt their inputs locally, the
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Hanzo Cloud evaluates AI models on ciphertexts without decryption, and only the
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client can decrypt the result. We implement two FHE schemes optimized for
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different model architectures: (i)~CKKS (Cheon--Kim--Kim--Song) for neural
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network inference, supporting approximate fixed-point arithmetic on encrypted
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vectors with SIMD batching, and (ii)~TFHE (Torus FHE) for decision tree and
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gradient-boosted model evaluation, supporting exact Boolean and integer
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operations with programmable bootstrapping. We describe GPU acceleration of FHE
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operations using the Candle runtime~\cite{hanzocandle}, achieving 14$\times$
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speedup over CPU-only evaluation on Number Theoretic Transform (NTT) kernels.
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Production deployment on Hanzo ACI~\cite{hanzoaci} demonstrates encrypted
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inference on a 4-layer neural network in 1.8 seconds (CKKS) and encrypted
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XGBoost evaluation with 500 trees in 0.9 seconds (TFHE), enabling practical
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confidential AI for healthcare, finance, and regulated industries. The system
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integrates with the Lux TFHE infrastructure~\cite{luxtfhe} for on-chain
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verification of encrypted computation proofs.
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\end{abstract}
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% ============================================================================
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\input{sections/01-introduction}
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\input{sections/02-fhe-background}
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\input{sections/03-scheme-selection}
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\input{sections/04-gpu-acceleration}
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\input{sections/05-encrypted-neural-network-inference}
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\input{sections/06-encrypted-decision-tree-inference}
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\input{sections/07-integration-with-hanzo-and-lux-infrastru}
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\input{sections/08-production-results}
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\input{sections/09-security-analysis}
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\input{sections/10-related-work}
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\input{sections/99-conclusion}
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\end{document}
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