Selected work

Representative Papers

Verification of Lightning Network Channel Balances with Trusted Execution Environments (TEE)

Dec 12, 2025

This work addresses the fundamental tension between channel liquidity state privacy and third-party verifiability in the Lightning Network (LN). We propose the first dual-assurance verification framework integrating Trusted Execution Environment (TEE) remote attestation with zero-knowledge Transport Layer Security (zkTLS). Methodologically, we design a synergistic mechanism of Hot Proofs—real-time enclave-resident balance attestations leveraging SGX or SEV—and Cold Proofs—on-chain settlement records—precisely delineating security boundaries and trade-offs. We further implement a Balanced Reporting API and zkTLS-based transport-layer verification to jointly ensure hardware-enforced integrity and end-to-end communication privacy. Our key contribution is the first deep integration of TEEs and zkTLS for off-chain liquidity auditing, eliminating risks of node tampering and third-party API leakage. Empirical evaluation demonstrates bounded verification latency, enabling high-confidence, non-intrusive financial capacity verification. The framework delivers a deployable, trustworthy verification infrastructure for LN auditors, service providers, and node operators.

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Transfer Orthology Networks

Oct 17, 2025

This study addresses the low utilization efficiency of transcriptomic data and poor interpretability of knowledge transfer in cross-species phenotypic prediction. We propose an interpretable transfer learning framework grounded in orthologous gene relationships. Methodologically, we construct a bipartite graph modeling orthology between source and target species’ genes, and design a learnable species transformation layer constrained by the adjacency matrix mask, integrated with a pretrained feedforward network to enable directed mapping of gene expression spaces and downstream phenotypic prediction. Our key contribution is the first incorporation of bipartite graph structural priors as explicit weight constraints in neural networks—endowing the transformation layer with both transfer capability and functional orthology interpretability. Empirical evaluation on synthetic data demonstrates superior predictive performance and enhanced biological plausibility over baseline models. The framework establishes an interpretable, scalable computational paradigm for subsequent experimental validation.

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Recent publications

Latest Papers

Verification of Lightning Network Channel Balances with Trusted Execution Environments (TEE)

Dec 12, 2025

This work addresses the fundamental tension between channel liquidity state privacy and third-party verifiability in the Lightning Network (LN). We propose the first dual-assurance verification framework integrating Trusted Execution Environment (TEE) remote attestation with zero-knowledge Transport Layer Security (zkTLS). Methodologically, we design a synergistic mechanism of Hot Proofs—real-time enclave-resident balance attestations leveraging SGX or SEV—and Cold Proofs—on-chain settlement records—precisely delineating security boundaries and trade-offs. We further implement a Balanced Reporting API and zkTLS-based transport-layer verification to jointly ensure hardware-enforced integrity and end-to-end communication privacy. Our key contribution is the first deep integration of TEEs and zkTLS for off-chain liquidity auditing, eliminating risks of node tampering and third-party API leakage. Empirical evaluation demonstrates bounded verification latency, enabling high-confidence, non-intrusive financial capacity verification. The framework delivers a deployable, trustworthy verification infrastructure for LN auditors, service providers, and node operators.

0 citationsRead paper

Transfer Orthology Networks

Oct 17, 2025

This study addresses the low utilization efficiency of transcriptomic data and poor interpretability of knowledge transfer in cross-species phenotypic prediction. We propose an interpretable transfer learning framework grounded in orthologous gene relationships. Methodologically, we construct a bipartite graph modeling orthology between source and target species’ genes, and design a learnable species transformation layer constrained by the adjacency matrix mask, integrated with a pretrained feedforward network to enable directed mapping of gene expression spaces and downstream phenotypic prediction. Our key contribution is the first incorporation of bipartite graph structural priors as explicit weight constraints in neural networks—endowing the transformation layer with both transfer capability and functional orthology interpretability. Empirical evaluation on synthetic data demonstrates superior predictive performance and enhanced biological plausibility over baseline models. The framework establishes an interpretable, scalable computational paradigm for subsequent experimental validation.

0 citationsRead paper