arXiv subjects

Zhiguang Shan

Publications and source records attributed to Zhiguang Shan.

3 recordsLinked to original sources

arXiv2026

Accountable yet Anonymous AI Agents - Split-Knowledge Binding in National Agent-Identity Layer in China

The emerging infrastructure for AI-agent identity has converged, in industry practice and research proposals alike, on a single resolution of the tension between accountability and privacy: make every agent identifiable. We document a national system in China -- built as national infrastructure and scheduled for public launch in Q3 2026 -- that occupies a different and underexplored point in the same design space: an agent is associated with a verified legal principal without that principal being disclosed to any business-layer participant. Re-identification is possible only to a legal authority acting through due process, by separately compelling two distinct government agencies, neither of which can re-identify alone. We name the mechanism split-knowledge binding and are candid that it is conditional: the separation is structural and procedural, not cryptographic, and a state empowered to compel both agencies can re-identify. The paper makes five contributions: (1) split-knowledge binding, an institutional rather than cryptographic separation for escrowed accountability; (2) the ex-post attribution thesis, the argued claim that only attribution-based accountability carries legal force for AI agent actions with legal consequences; (3) the accountability surface, a design concept identifying which agent actions leave identity-bearing traces; (4) a proportionality framework for identity escrow, a decision structure selecting among three trust architectures; and (5) the reflexive jurisdiction method, an evaluative standard administered to the paper's own deployment. The system is evidence of feasibility at national scale; the framework is the instrument by which any deployment -- including this one -- should be judged.

cs.CY↗

arXiv2025

CCN: Decentralized Cross-Chain Channel Networks Supporting Secure and Privacy-Preserving Multi-Hop Interactions

Cross-chain technology enables interoperability among otherwise isolated blockchains, supporting interactions across heterogeneous networks. Similar to how multi-hop communication became fundamental in the evolution of the Internet, the demand for multi-hop cross-chain interactions is gaining increasing attention. However, this growing demand introduces new security and privacy challenges. On the security side, multi-hop interactions depend on the availability of multiple participating nodes. If any node becomes temporarily offline during execution, the protocol may fail to complete correctly, leading to settlement failure or fund loss. On the privacy side, the need for on-chain transparency to validate intermediate states may unintentionally leak linkable information, compromising the unlinkability of user interactions. In this paper, we propose the Cross-Chain Channel Network (CCN), a decentralized network designed to support secure and privacy-preserving multi-hop cross-chain transactions. Through experimental evaluation, we identify two critical types of offline failures, referred to as active and passive offline cases, which have not been adequately addressed by existing solutions. To mitigate these issues, we introduce R-HTLC, a core protocol within CCN. R-HTLC incorporates an hourglass mechanism and a multi-path refund strategy to ensure settlement correctness even when some nodes go offline during execution. Importantly, CCN addresses not only the correctness under offline conditions but also maintains unlinkability in such adversarial settings. To overcome this, CCN leverages zero-knowledge proofs and off-chain coordination, ensuring that interaction relationships remain indistinguishable even when certain nodes are temporarily offline.

cs.CR↗

arXiv2023

Impact-Oriented Contextual Scholar Profiling using Self-Citation Graphs

Quantitatively profiling a scholar's scientific impact is important to modern research society. Current practices with bibliometric indicators (e.g., h-index), lists, and networks perform well at scholar ranking, but do not provide structured context for scholar-centric, analytical tasks such as profile reasoning and understanding. This work presents GeneticFlow (GF), a suite of novel graph-based scholar profiles that fulfill three essential requirements: structured-context, scholar-centric, and evolution-rich. We propose a framework to compute GF over large-scale academic data sources with millions of scholars. The framework encompasses a new unsupervised advisor-advisee detection algorithm, a well-engineered citation type classifier using interpretable features, and a fine-tuned graph neural network (GNN) model. Evaluations are conducted on the real-world task of scientific award inference. Experiment outcomes show that the F1 score of best GF profile significantly outperforms alternative methods of impact indicators and bibliometric networks in all the 6 computer science fields considered. Moreover, the core GF profiles, with 63.6%-66.5% nodes and 12.5%-29.9% edges of the full profile, still significantly outrun existing methods in 5 out of 6 fields studied. Visualization of GF profiling result also reveals human explainable patterns for high-impact scholars.

cs.DL↗