Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability
arXiv:2503.07570v3 Announce Type: replace-cross Abstract: Deep learning, when integrated with a large amount of training data, has the potential to outperform machine learning in terms of high accuracy. Recently, privacy-preserving deep learning has drawn significant attention of the research community. Different privacy notions in deep learning include privacy of data provided by data-owners and privacy of parameters and/or hyperparameters of the underlying neural network. Federated learning is a popular privacy-preserving execution environment where data-owners participate in learning the pa
The increasing focus on data privacy and the auditability of AI systems, coupled with the rising complexity of multi-party deep learning, drives the need for solutions like Split-n-Chain.
This development allows for more secure and transparent AI training across distributed data sources, mitigating risks associated with data privacy and model integrity in collaborative AI environments.
The integration of blockchain for auditability in privacy-preserving split learning offers a new paradigm for building trustworthy, multi-institutional AI applications.
- · Healthcare sector
- · Financial services
- · AI ethics and compliance platforms
- · Privacy-enhancing technology developers
- · Centralized data custodians
- · AI systems lacking audit trails
- · Organizations with lax data governance
Increased adoption of privacy-preserving AI techniques in sensitive data domains.
Development of regulatory frameworks that mandate blockchain-based auditability for collaborative AI models.
Enhanced trust in AI systems could accelerate their integration into critical infrastructure and governmental decision-making.
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Read at arXiv cs.LG