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Model Complexity Reduction for ZKML Healthcare Applications: Privacy Protection and Inference Optimization for ZKML
Sathya Krishnasamy1, Ilangovan Govindarajan2
1President and Principal, ChainAim, Newington, Connecticut, USA.
Web 3.0, utilizing decentralized networks and AI, faces adoption barriers. This paper explores zero-knowledge machine learning (ZKML) as a solution for privacy and efficiency in global healthcare data collection.
Area of Science:
- Decentralized Systems and Artificial Intelligence
- Cryptographic Applications in Healthcare
Background:
- Web 3.0 technologies, including decentralized networks and AI, are advancing but face significant adoption challenges.
- Previous work identified barriers and mitigations for Web 3.0 adoption in healthcare, focusing on privacy and design optimizations.
Purpose of the Study:
- To conceptualize the technical and operational feasibility of Zero-Knowledge Machine Learning (ZKML) in global healthcare.
- To implement a reference healthcare application using ZKML for high-volume data collection and patient-reported outcomes.
- To advance the use of machine learning models in decentralized healthcare architectures for enhanced data protection and efficiency.
Main Methods:
- Conceptualization of ZKML's technical and operational feasibility.
- Implementation of a reference healthcare system using synthetic International Consortium for Health Outcomes Measurement (ICHOM) data.
- Research on model complexity reduction for the ICHOM diabetes dataset.
Main Results:
- Demonstrated the conceptual feasibility of ZKML in a global healthcare context.
- Developed a reference implementation for high-volume data collection, including patient-reported outcomes.
- Reported model complexity reduction for the ICHOM diabetes dataset, enhancing ML model applicability.
Conclusions:
- ZKML presents a viable approach to address privacy and inference cost challenges in Web 3.0 healthcare applications.
- The study provides a foundation for applying ZKML in global healthcare standards, improving data protection and efficiency.
- Further development is needed to establish baselines for ZKML in widespread global healthcare adoption.
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