arXiv subjects

Yilin Sai

Publications and source records attributed to Yilin Sai.

3 recordsLinked to original sources

arXiv2026

SoK: Credential-Based Trust Management in Decentralized Ledger Systems

Trust management systems (TMS) are crucial for managing trust in distributed environments. The rise of decentralized systems and blockchain has sparked interest in credential-based decentralized trust management systems (DTMS). This paper bridges the gap between theory and practice through a systematic review of credential-based DTMS. We analyze existing DTMS solutions through multiple dimensions, including their architectural designs, credential mechanisms, and trust evaluation models. Our survey provides a detailed taxonomy of credential-based DTMS approaches and establishes comprehensive evaluation criteria for assessing DTMS implementations. Through extensive analysis of current systems and implementations, we identify critical challenges and promising research directions in the field. Our examination offers valuable insights for researchers and practitioners working on DTMS, particularly in areas such as access control, reputation systems, and blockchain-based trust frameworks.

cs.CR↗

arXiv2025

Is Your AI Truly Yours? Leveraging Blockchain for Copyrights, Provenance, and Lineage

As Artificial Intelligence (AI) integrates into diverse areas, particularly in content generation, ensuring rightful ownership and ethical use becomes paramount, AI service providers are expected to prioritize responsibly sourcing training data and obtaining licenses from data owners. However, existing studies primarily center on safeguarding static copyrights, which simply treat metadata/datasets as non-fungible items with transferable/trading capabilities, neglecting the dynamic nature of training procedures that can shape an ongoing trajectory. In this paper, we present \textsc{IBis}, a blockchain-based framework tailored for AI model training workflows. Our design can dynamically manage copyright compliance and data provenance in decentralized AI model training processes, ensuring that intellectual property rights are respected throughout iterative model enhancements and licensing updates. Technically, \textsc{IBis} integrates on-chain registries for datasets, licenses and models, alongside off-chain signing services to facilitate collaboration among multiple participants. Further, \textsc{IBis} provides APIs designed for seamless integration with existing contract management software, minimizing disruptions to established model training processes. We implement \textsc{IBis} using Daml on the Canton blockchain. Evaluation results showcase the feasibility and scalability of \textsc{IBis} across varying numbers of users, datasets, models, and licenses.

cs.CR↗

arXiv2023

An Empirical Study on Snapshot DAOs

Decentralized Autonomous Organization (DAO) is an organization constructed by automatically executed rules such as via smart contracts, holding features of the permissionless committee, transparent proposals, and fair contribution by stakeholders. As of Nov 2022, DAO has impacted over \$11.2B market caps. However, there are no substantial studies focused on this emerging field. To fill the gap, we start from the ground truth by empirically studying the breadth and depth of the DAO markets in mainstream public chain ecosystems in this paper. We dive into the most widely adoptable DAO launchpad, \textit{Snapshot}, which covers 95\% in the wild DAO projects for data collection and analysis. By integrating extensive enrolled DAOs and corresponding data measurements, we explore statistical data from Snapshot and try to demystify its undiscovered truths by delivering a series of summarised insights. We also present DAO status, patterns, distribution, and trends. To our knowledge, this is the first empirical study putting concentration on DAO spaces.

cs.CR↗