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Model Complexity Reduction for ZKML Healthcare applications | Privacy protection and inference optimization for ZKML applications: A reference implementation with synthetic ICHOM dataset
Sathya Krishnasamy, MS, Ilangovan Govindarajan, MD
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Web 3.0 represents the next significant evolution of the internet that embodies the underlying decentralized network architectures, distributed ledgers, and advanced AI
capabilities. Though the technologies are maturing rapidly, considerable barriers exist to high-scale adoption. The author discusses the barriers and the mitigations through specific technologies maturing to solve these issues; including privacy-preserving technologies, off-chain and on-chain design optimizations, and the multi-dimensional approach needed in planning and adopting these technologies.
The paper discusses how one
enabler, ZKML, combines two streams of technology that are merging in unique ways to address problems in privacy and the cost of inference. The authors have conceptualized the technical and operational feasibility and implemented a reference healthcare implementation using the synthetic ICHOM in the evaluation phase of a global healthcare setting for high-volume data collection, including patient-reported outcomes.
Model
complexity reduction is researched and reported for the ICHOM diabetes dataset to advance the usage of ML models in global standards of healthcare data collection in network decentralized architectures for increased data protection and efficiencies.
