Web3ID: A Privacy-Preserving and DApp-Oriented Decentralized Identity Framework for Web3.0
With the development of Web3.0, decentralized identity and other blockchain-based identity empower users with control, forming the foundational infrastructure for Web3.0 ecosystems. However, existing identity frameworks remain inadequate in addressing critical challenges such as on-chain privacy during identity management and utilization. While prior works like CanDID, Hades, and CertChain explore blockchain-based identity solutions, they fail to meet the specific reqirements of DApps. Moreover, users on blockchain always store their identity data and digital assets across multiple accounts and DApps, but current identity schemes cannot support the cross-account and DApp identity privacy-preserving utilization. To bridge this gap, we propose Web3ID, the first fully DApp-oriented identity framework. By analyzing Ethereum identity proposals and user behavior patterns, we design the Web3ID featuring: on-chain privacy-preserving identity aggregation protocol, provably secure attribute-based access control model, and zk-rollup enhanced off-chain identity management. Experiments demonstrate that Web3ID enables privacy-preserving identity management and authentication on-chain, and guaranteeing access control completeness. The prototype system achieves a 100× improvement in proof/verification efficiency and reduces storage overhead by 85× compared to pure on-chain implementation through off-chain optimization techniques. Moreover, in comparison with other identity privacy solutions, Web3ID exhibits the lowest gas consumption during on-chain utilization and shows strong scalability. As a fully decentralized identity framework supporting end-to-end DApp integration, Web3ID advances Web3.0’s vision of user sovereignty, decentralization, and interoperability. This work establishes both theoretical and practical foundations for on-chain identity systems in Web3.0 ecosystems.
Authors
- Zhi Guan (ORCID: https://orcid.org/0000-0002-6144-4815)
- Jiakun Hao (ORCID: https://orcid.org/0000-0001-6514-9410)
- Xiang Peng (ORCID: https://orcid.org/0009-0002-5659-6571)
- L. Xia (ORCID: https://orcid.org/0009-0001-1374-2519)
- Jianbo Gao (ORCID: https://orcid.org/0000-0002-6760-5552)
- Zhong Chen (ORCID: https://orcid.org/0000-0002-5785-2912)
- Yifei He
Institutions
- Peking University (CN)
- Beijing Jiaotong University (CN)
- Beijing Institute of Big Data Research (CN)
Publication Details
- Journal
- ACM Transactions on the Web
- Published
- 2025-10-10
- DOI
- https://doi.org/10.1145/3771556
- Primary Topic
- Privacy-Preserving Technologies in Data
- Type
- article
- Field-Weighted Citation Impact
- 0.00