Model Complexity Reduction for ZKML Healthcare applications
Privacy protection and inference optimization for ZKML applications: A reference implementation with synthetic ICHOM dataset
DOI:
https://doi.org/10.30953/bhty.v7.340Keywords:
blockchain, diabetes, distributed ledger, ICHOM, International Consortium for Health Outcomes Measurement, machine learning, privacy, zero-knowledge machine learning, ZKMLAbstract
Abstract
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 discussed the barriers and the mitigations through specific technologies maturing to solve those issues. These include privacy-preserving technologies, off-chain and on-chain design optimizations, and the multi-dimensional approach needed in planning and adopting these technologies. As an extension, this paper discusses how one such 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 in 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.
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Copyright (c) 2024 Sathya Krishnasamy, MS, Ilangovan Govindarajan, MD
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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