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Locked canonical naming for the Lux FHE product line: Lux FHE = FHE framework umbrella Torus = compiler / runtime TorusVM = core FHE runtime virtual machine TorusEVM = encrypted EVM (this rename) TorusML = private ML TorusNet = threshold/decryption network Zoo FHE / zoo-fhe-ai paper now references TorusEVM as the on-chain FHE-EVM that Zoo extends with CKKS support + ML inference at the T-Chain layer.
50 lines
2.3 KiB
TeX
50 lines
2.3 KiB
TeX
\section*{Acknowledgments}
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This work was supported by Zoo Labs Foundation. We thank the Lux Network team for the FHE precompile infrastructure (\texttt{lux/precompile/fhe}), the \texttt{lux/fhe} Go TFHE library on \texttt{luxfi/lattice}, and the \texttt{luxcpp/gpu} dispatcher + \texttt{lux-private/gpu-kernels} acceleration substrate that this work consumes unchanged.
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\bibliographystyle{plain}
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\begin{thebibliography}{99}
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\bibitem{chillotti2020tfhe}
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I. Chillotti, N. Gama, M. Georgieva, and M. Izabach\`{e}ne, ``TFHE: Fast Fully Homomorphic Encryption over the Torus,'' \textit{Journal of Cryptology}, vol. 33, pp. 34--91, 2020.
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\bibitem{cheon2017ckks}
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J.H. Cheon, A. Kim, M. Kim, and Y. Song, ``Homomorphic Encryption for Arithmetic of Approximate Numbers,'' \textit{ASIACRYPT}, 2017.
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\bibitem{lux-tfhe}
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Lux Industries, ``Lux TFHE: Native Fully Homomorphic Encryption on the Lux Network,'' Technical Report, 2025.
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\bibitem{lux-fhevm}
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Lux Industries, ``TorusEVM: FHE-Enabled Ethereum Virtual Machine,'' Technical Report, 2025.
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\bibitem{zoo-federated-wildlife}
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Z. Kelling et al., ``Federated Wildlife Monitoring on the Zoo Network,'' Zoo Labs Foundation, 2025.
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\bibitem{zoo-conservation-ai}
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Z. Kelling et al., ``Conservation AI: Decentralized Species Monitoring and Habitat Analysis,'' Zoo Labs Foundation, 2025.
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\bibitem{lux-evmgpu-benchmark}
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Lux Industries, ``GPU-Accelerated EVM: Benchmarks and Architecture,'' Technical Report, 2025.
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\bibitem{albrecht2018hes}
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M. Albrecht et al., ``Homomorphic Encryption Standard,'' \textit{Protecting Privacy through Homomorphic Encryption}, Springer, 2021.
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\bibitem{gilad2016cryptonets}
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R. Gilad-Bachrach et al., ``CryptoNets: Applying Neural Networks to Encrypted Data,'' \textit{ICML}, 2016.
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\bibitem{brutzkus2019low}
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A. Brutzkus, R. Gilad-Bachrach, and O. Elisha, ``Low Latency Privacy Preserving Inference,'' \textit{ICML}, 2019.
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\bibitem{mcmahan2017communication}
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B. McMahan et al., ``Communication-Efficient Learning of Deep Networks from Decentralized Data,'' \textit{AISTATS}, 2017.
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\bibitem{zhu2019deep}
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L. Zhu, Z. Liu, and S. Han, ``Deep Leakage from Gradients,'' \textit{NeurIPS}, 2019.
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\bibitem{lux-mchain-mpc}
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Lux Industries, ``M-Chain: Threshold Multi-Party Computation on Lux Network,'' Technical Report, 2025.
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\end{thebibliography}
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