인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 저자정보
피인용 0
초록·키워드
In blockchain-based environments, data exchange and digital asset transfer are not merely processes of transmitting data, but complex procedures that simultaneously verify the ownership, authorization, integrity, and provenance of the underlying data. This study classifies ownership verification mechanisms in blockchain-based data exchange into five stages — identity verification, ownership verification, authorization verification, provenance verification, and privacy-preserving verification — and further subdivides these into nine technical mechanisms: cryptographic key-based verification, smart contract- based verification, distributed ledger-based provenance verification, hash-based integrity verification, Decentralized Identifier (DID)-based verification, Verifiable Credential (VC)-based authorization verification, Zero-Knowledge Proof (ZKP)-based verification, verification in Real-World Asset (RWA) tokenization environments, and verification in AI agent-to-agent data exchange. For each mechanism, the underlying theoretical principles and verification procedures are examined, and their practical applications are illustrated through industry and standardization cases. This study further proposes an integrated seven-layer ownership verification framework comprising the Identity, Ownership, Authorization, Integrity, Provenance, Privacy, and Governance layers. The analysis shows that blockchain-based ownership verification is evolving from a single technology into a multi-layered mechanism that organically combines multiple cryptographic and institutional technologies. In particular, with the expansion of AI agent economies and RWA tokenization, the need for a governance framework integrating digital signatures, DID, VC, smart contracts, ZKP, distributed ledgers, and reputation management is growing. The findings of this study may serve as a theoretical and practical foundation for designing future blockchain-based data transaction standards and AI agent trustworthiness governance standards, including those under discussion in ISO/TC 307.
본문·목차
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