A2-DIDM: Privacy-preserving Accumulator-enabled Auditing for Distributed Identity of DNN Model
Summary: arXiv:2405.04108v2 Announce Type: replace-cross
In recent years, the rapid advancement of Generative Artificial Intelligence (GenAI) has significantly enhanced the commercialization of Deep Neural Network (DNN) models. This has opened up avenues for licensing and trading DNN models, leading to improved performance metrics. However, alongside these advancements, there are growing concerns regarding the protection of model ownership. Unauthorized replications or misuse of DNN models pose serious threats to the rights of their creators.
Model identity auditing has emerged as a critical challenge in safeguarding the ownership of DNN models. Verifying the authenticity and integrity of models is essential to overcome obstacles that threaten the rights of model owners. In light of these concerns, a new framework has been proposed: the A2-DIDM, which stands for Accumulator-enabled Auditing for Decentralized Identity of DNN Models.
Overview of A2-DIDM
The A2-DIDM framework leverages advanced technologies, including blockchain and zero-knowledge proofs, to maintain data and functional privacy while ensuring lightweight ownership verification on the blockchain. The core of this model revolves around the establishment of identity records through the configuration of model weight checkpoints, which are fortified with zero-knowledge proofs.
Key Features
- Incremental State Changes: The model incorporates predicates that capture incremental state changes in model weight checkpoints, ensuring a comprehensive and dynamic auditing process.
- Computational Integrity: A2-DIDM guarantees computational integrity throughout the DNN training process, thereby preserving the uniqueness of the weight checkpoint sequence.
- Privacy Protection: The framework effectively addresses privacy concerns associated with decentralized identity, ensuring that sensitive information remains secure.
Security and Robustness
The proposed model has undergone systematic analysis to assess its security and robustness. The findings indicate that A2-DIDM not only provides a secure environment for DNN model owners but also enhances the overall usability and effectiveness of auditing DNN model identities.
Conclusion
The A2-DIDM framework represents a significant step forward in addressing the challenges faced by DNN model owners in the era of GenAI. By combining blockchain technology and zero-knowledge proofs, it offers a robust solution that safeguards ownership while facilitating the responsible use of DNN models. The implementation details and source code for A2-DIDM are available at GitHub.