mirror of
https://github.com/lux-apps/papers.git
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zeekayandHanzo Dev
178febad68
Every rendered "T-Chain" / "ThresholdVM" / "teleportvm" reference across the paper corpus is rewritten to the correct canonical chain by concern: threshold signing / MPC / bridge custody / key recovery -> M-Chain (MVM); FHE / encrypted compute / threshold decryption -> F-Chain (FVM); teleport / cross-chain messaging -> B-Chain (bridgevm); consensus Pulsar stays Q-Chain. Section lux-trust-kernel/03-t-chain-threshold-governance.tex renamed -> 03-f-chain-encrypted-policy.tex (with \input + xref fixes). Braided "T-Chain" sections split by concern (M vs F). Only lp-020's chain-separation rationale and the shared lux/chains/thresholdvm library path retain the name, to record the removal. All changed papers rebuilt (pdflatex, rc=0, no undefined refs). §09 of lux-dao-governance-framework reframed: fabricated "production deployment" metrics ($50M/$240k/$12M, survey %s, "as of Oct 2024") relabeled as illustrative design configurations with no invented replacements; reality noted (DAO treasury is a 1/1 Safe); conclusion echo + one "first production" overclaim softened. This commit also captures concurrent corpus workstreams on the same tree. Co-authored-by: Hanzo Dev <[email protected]>
57 lines
3.0 KiB
TeX
57 lines
3.0 KiB
TeX
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%% ===================================================================
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%% LP-134 canonical naming (2025-12-15):
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%% Per LP-134 (Lux Chain Topology), F-Chain has been split into:
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%% M-Chain — MPC ceremonies for bridge custody of EXTERNAL wallets
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%% (CGGMP21, FROST, Pulsar-general)
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%% F-Chain — FHE compute + TFHE bootstrap-key generation (encrypted EVM)
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%% F-Chain is REMOVED entirely; teleport is B-Chain (bridgevm, LP-6000). No teleportvm.
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%% Where this paper says "F-Chain MPC" / "F-Chain threshold governance" /
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%% "F-Chain TFHE" / "F-Chain custody", read as M-Chain (MPC) or F-Chain (FHE).
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%% ===================================================================
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\section{Private Inference}
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\label{sec:private-inference}
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Sovereign agents must be able to perform model inference without revealing their inputs, outputs, or reasoning to the compute provider. We achieve this through CKKS-encrypted inference with threshold decryption.
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\subsection{Encrypted Model Inputs}
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The agent encrypts its inference input $x$ using the CKKS scheme with parameters suitable for the model's precision requirements:
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\begin{equation}\label{eq:enc-input}
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\tilde{x} = \text{CKKS.Enc}(\text{pk}_{\text{agent}}, x; N, q, \Delta)
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\end{equation}
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where $N$ is the polynomial degree, $q$ is the ciphertext modulus, and $\Delta$ is the scaling factor. The encrypted input is submitted to a compute node along with a reference to the model and inference parameters.
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\subsection{Homomorphic Evaluation}
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The compute node evaluates the model $f$ on the encrypted input:
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\begin{equation}\label{eq:he-eval}
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\tilde{y} = f(\tilde{x})
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\end{equation}
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For transformer-based models, this requires approximating non-polynomial activations (GELU, softmax) via polynomial approximations~\cite{lee2022privacy}. The computational overhead is significant (100--1000$\times$ for current FHE schemes) but is amortized across batch inference and is acceptable for high-value reasoning tasks where privacy justifies cost.
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\subsection{Threshold Decryption}
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The encrypted result $\tilde{y}$ is decrypted via a $(k, n)$-threshold scheme managed by the F-Chain. The agent's FHE secret key is secret-shared among $n$ F-Chain validators using Shamir's Secret Sharing:
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\begin{equation}\label{eq:threshold-dec}
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y = \text{CKKS.Dec}\Big(\text{TSS.Reconstruct}\big(\{s_i\}_{i \in S}, |S| \geq k\big), \tilde{y}\Big)
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\end{equation}
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No single validator can decrypt the result. The agent receives the plaintext output; the compute node and individual validators learn nothing.
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\subsection{Inference Receipts}
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Every inference produces a verifiable receipt:
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\begin{equation}\label{eq:receipt}
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\rho = \big(H(\tilde{x}), H(\tilde{y}), \text{model\_id}, t, \sigma_{\text{compute}}\big)
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\end{equation}
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The receipt is anchored on-chain and serves as proof that the agent performed inference at time $t$ on model $\text{model\_id}$, without revealing the inputs or outputs. This creates an auditable computational history without sacrificing privacy.
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