1 (page number not for citation purpose) TECHNICAL BRIEFS & SHORT REPORTS Model Complexity Reduction for ZKML Healthcare Applications: Privacy Protection and Inference Optimization for ZKML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;1 and Ilangovan Govindarajan, MD2 1President and Principal, ChainAim, Newington, Connecticut, USA, 2Chief Medical Officer, GuardianMedx, Las Vegas, Nevada Corresponding Author: Sathya Krishnasamy, Email: sathya.krishnasamy@chainaim.com DOI: https://doi.org/10.30953/bhty.v7.340 Keywords: blockchain, diabetes, distributed ledger, model complexity reduction privacy, machine learning, ICHOM, International Consortium for Health Outcomes Measurement, ZKML, zero-knowledge machine learning 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 discusses the barriers and the mitigations through specific technologies maturing to solve those issues in an earlier paper titled Moving Beyond POCs and Pilots, published in 2023 in Blockchain in Healthcare Today. 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 one such enabler, zero knowledge machine learning (ZKML), which merges two streams of technology in unique ways to address problems in privacy and the cost of inference. Zero-knowledge proofs (ZKP) allow one party to prove the validity of a statement to another party without revealing any additional information about the statement itself. The ZKML combines the cryptographic principle of ZKP with machine learning (ML) techniques. It is still a maturing technology and needs baselines for applications in global healthcare. In this effort, the authors conceptualize the technical and operational fea- sibility of using ZKML and implement a reference healthcare implementation using the synthetic International Consortium for Health Outcomes Measurement (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 health- care data collection in network decentralized architectures for increased data protection and efficiencies. Submitted: August 1, 2024; Accepted: August 25, 2024; Published: August 31, 2024 The sizable amount of data collection in recent years has produced unprecedented analytical capabilities. However,1with the rapid increase in data ingestion and machine learning (ML), particularly in centralized systems, data privacy and security con- cerns have increased significantly with excessive data movements, sometimes not truly needed. As central data repositories grow larger, they become attractive targets. Healthcare data breaches have become increasingly Appendix common. There have been many high-profile incidents, and the trend has been continuous. A key strategy for mitigating the risks associated with healthcare data is restricting access to data at the source and reducing unnecessary data movements. These include data access using role-based access, data encryption, minimal data transfer, and regular audits. While these are paramount, it is also critical to devise collaboration systems that could communicate across decentralized networks. Zero-knowl- edge proofs (ZKP) can be designed for evidence and verifi- cation between systems, even before the blockchains. These Blockchain in Healthcare Today ISSN 2573-8240 https://orcid.org/0009-0000-1420-1098 https://orcid.org/ 0009-0005-3825-0923 mailto:sathya.krishnasamy@chainaim.com https://doi.org/10.30953/bhty.v7.340 Citation: Blockchain in Healthcare Today 2024, 7: 340 - https://doi.org/10.30953/bhty.v7.3402 (page number not for citation purpose) S. Krishnasamy and I. Govindarajan ZKPs allow one party to prove the validity of a statement to another party without revealing any additional informa- tion about the statement itself and have been increasing in popularity as distributed ledger technology matures and has helped scale blockchain implementations. Zero-knowledge machine learning (ZKML) represents a revolutionary fusion of cryptographic and ML tech- nologies. It combines the cryptographic principle of ZKP with ML techniques. By integrating ZKP with ML, ZKML ensures that sensitive data remain confidential while still allowing for the development and utilization of predictive models. This integration is increasingly rele- vant where data privacy and security are paramount, such as in healthcare systems. In practical terms, ZKML allows multiple entities to collaborate without compromising the confidentiality of their proprietary information. This means organizations can collaboratively train and utilize ML models on pri- vate datasets without exposing the data. For example, a medical research organization could aggregate data from various hospitals to develop a robust predictive model for disease without any hospital sharing its patient data. The use of cryptographic proofs ensures that the data remain secure and private throughout the process. Currently, ZKML is still in nascent research and devel- opment. While ZKPs have been a topic of cryptographic research since the 1980s, their application to ML is new and complex. This technology faces challenges related to computational efficiency and resource demands. Implementing ZKPs can be resource-intensive, increasing processing times and costs. Model complexity reduction is a recent technique to reduce model complexity, hence reducing computa- tion times. It has been attempted using simpler models from Kaggle. The International Consortium for Health Outcomes Measurement (ICHOM) is dedicated to developing standard sets of outcome measures that can be used globally to assess the quality of care for various medical conditions. This research aims to evaluate the usage of a global standard healthcare dataset (ICHOM) and apply model complexity reduction for a real-world, non-trivial example to a synthetic dataset in the ICHOM schema. Hence, this research will be a reference for devel- oping further models and optimizations that will make it conducive to optimizing the complexity and use of ZKML for many use cases in healthcare that need privacy and collaborative decision-making. A Paradigm Shift: Decentralized Systems, AI Models at Source, and Collaborative Decision-Making As we approach emerging architectures built upon decentralized systems, restricting data at source and understanding ways to work with the data at source via privacy-protected ML becomes essential. Data are federated anyway in systems such as U.S. healthcare, and effective mechanisms are needed to enable federated learning, address privacy and security issues, and reduce computational overheads. By sharing model updates rather than data, federated learning enhances privacy. Similarly, with distributed ledgers, an increasingly popular design concept is to provide proof to verification systems through ZKP. Though not a new concept, this method has success- fully scaled blockchain systems and is becoming increas- ingly popular for next-generation distributed ledgers. These mechanisms also reduce data transmission risk and align with data protection regulations and collaborative deci- sion-making. The ZKML is a promising emerging technol- ogy that integrates AI/ML (artificial intelligence/machine learning) technologies into distributed ledgers. Impact of ZKML on Healthcare Privacy Healthcare data are sensitive; therefore, privacy is the first design principle in healthcare systems. With ZKML, healthcare providers can share insights derived from ML  models without exposing the underlying patient data. For example, a hospital could use a ZKML model to predict patient outcomes. The model processes the data and generates predictions, while the ZKP ensures that these predictions are accurate without revealing specific patient information. ZKML allows different entities to collaboratively compute results without dis- closing their individual data. This is particularly useful in healthcare research, where multiple institutions may wish to combine their data to improve disease predictions without  compromising patient confidentiality. As data privacy concerns intensify and the need for secure ML models grows, integrating advanced privacy-preserving technologies becomes crucial. Literature Review The ZKPs are cryptographic methods that enable one party (the prover) to convince another party (the verifier) that a statement is true without revealing any additional information beyond the validity of the statement itself. Introduced by Goldwasser, Micali, and Rackoff (1985),1 they established the theoretical framework for interac- tive proofs and ZKPs, demonstrating their feasibility and foundational importance. Further work by Fiat and Shamir2 extended it to non-interactive ZKP, which does not require multiple rounds of communication between the prover and verifier. Recent advancements in ZKPs, such as Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs) by Ben-Sasson and colleagues3,4 have enhanced their efficiency and scalability and opened possibilities for real-world applications. Recently, ML showed success in modeling [ healthcare prediction tasks, ranging from disease diagnosis and https://doi.org/10.30953/bhty.v7.340 Citation: Blockchain in Healthcare Today 2024, 7: 340 - https://doi.org/10.30953/bhty.v7.340 3 (page number not for citation purpose) Complexity reduction for ZKML prognosis to patient treatment. Guerra and colleagues5 reviewed privacy-preserving ML literature for training and inference and concluded healthcare datasets are diverse and a fraction of them considered validating with independent standard datasets. They indicated the risks of centralized training for federated learning as a risk and also called out the need for collaboration between differ- ent entities across multiple roles of ML scientists, health- care practitioners, and privacy and security experts, which need privacy-preserving mechanisms to work together over distributed ledgers. A newly emerging technology, ZKML applies ZKP to ML for privacy, ensuring that sensitive data remain confidential while allowing for developing and utilizing predictive models for privacy and collaboration. The ZKP, as such, are computationally expensive and get even more computationally expensive for ML inference proofs. Recent work from Alejandro Martinez Gator6 produced a model complexity reducer (MCR) library and illustrated its reference implementations on sam- ple datasets. However, from a global healthcare per- spective, there is a need to validate and benchmark this effort with a high-scale standardized global healthcare schema. Methods Research goals Guardian Medx is a comprehensive care plan offering personalized medical care with continuous monitoring and assistance. The goal is to improve the wellness of seniors and reduce hospitalizations in the south- ern region of India. The principals aimed to follow an evaluation process first to learn from earlier work done in diabetic care in South India7, and identify the cultural elements involved in the disease dynamics, from diagnosis to treatment to adherence to continu- ous maintenance, and to find the suitable standardized format to capture both clinical and patient-reported outcomes for a holistic basis. The intent is to find the data collection schema conducive to privacy-preserv- ing collaboration in emerging Web 3 technologies, including ML for data analysis and privacy-preserving consent and cooperation. The starting point for this research is to find a high-scale standardized global healthcare schema that addresses the challenges indicated above, allowing for adequate data  collection and identifying the privacy-preserved constructs needed for collaborative learning purposes at a global level. The specific research goals include the following: • Learn from earlier research and create a data collection approach that captures the cultural elements of diabetic care. • Identify a specific data schema that is standardized and can be used for data collection and privacy-preserved learning at a global scale. • Explore ZKML as the model for that global data schema. • Identify the parameters, limitations, and mitigations using MCR. • Baseline the data schema with synthetic data. After reviewing the literature on diabetic care delivery in India7, the big data collection and ML effort8 and stan- dardization formats and adaptability reports9,10 the data schema was decided to be the ICHOM schema, with the adaption needed. The Ichom Datasets and Impact on Global Healthcare for Diabetes The ICHOM is dedicated to developing standard sets of outcome measures that can be used globally to assess the quality of care for various medical conditions. For diabetes, a chronic condition with significant variation in presentation and management across different cultures and healthcare systems, this standardization is crucial. Diabetes care can vary significantly due to cultural differences, socioeconomic factors, and healthcare infra- structure. For instance, the management strategies and patient outcomes in a high-income country might not directly apply to a low-income setting with different resources and cultural attitudes toward health. ICHOM’s dataset addresses this challenge by allowing healthcare systems to adapt standardized measures to local contexts. This cultural tailoring ensures that the outcome mea- sures are relevant and practical in diverse settings, thereby improving the dataset’s utility and impact on global health outcomes. By providing a standard set of metrics, ICHOM helps identify gaps in care and outcomes across different regions and populations. By adopting a uniform approach to measuring outcomes such as blood glucose levels, qual- ity of life, and complication rates, the ICHOM diabetes dataset enables healthcare providers to benchmark their performance against global standards, identify best prac- tices, and improve patient care and population health cohorts. Given the nature of the data and the standard- ization effort, the principals initiated the evaluation with synthetic datasets. They used the ICHOM older popu- lation and diabetic datasets to provide insights into the complex nature of diabetes care and its impact on patient outcomes. This research offers a comprehensive data-oriented approach to diabetes management by collecting and analyzing data on demographics, diagnosis, lifestyle and social factors, treatment methods, diabetes control, acute events, chronic complications, and patient-reported https://doi.org/10.30953/bhty.v7.340 Citation: Blockchain in Healthcare Today 2024, 7: 340 - https://doi.org/10.30953/bhty.v7.3404 (page number not for citation purpose) S. Krishnasamy and I. Govindarajan outcomes. It seeks to illuminate the intricate interplay of factors affecting diabetes management and optimize dia- betes care cost-effectively with early diagnosis and remote and continuous monitoring at scale. As part of the data evaluation exercise for the research, synthetic datasets with hypothetical values are produced from earlier literature and the researchers’ experiences. Data adequacy, baseline, and meaningful data mappings using the ICHOM diabetes datasets V5.0 are established for practical deployments in the Indian cultural setting. These datasets were selected from the ICHOM V5 dia- betic dataset. Synthetic data was set up for 100 patients, with several iterations of data and clinical validations. Exploratory data analysis with univariate and bivariate analysis and correlations was developed and analyzed. The model was built using a Light Gradient Boost- ing Machine (LightGBM) regressor—an open-source, high-performance gradient boosting framework designed for efficient and scalable ML tasks. It is specially tai- lored for speed and accuracy, making it a popular choice for both structured and unstructured data in diverse domains. Key characteristics of LightGBM include its ability to handle large datasets with millions of rows and columns, support for parallel and distributed computing, and optimized gradient-boosting algorithms using histo- gram-based techniques and leaf-wise tree growth. A crucial aspect of ZKML is model complexity reduc- tion, which is critical with current inference costs and distributed ledger technology scalability to make these models more efficient and practical for real-world appli- cations. This article explores the concept of model com- plexity reduction in the context of ZKML, with specific examples and a focus on its critical role in healthcare. Model complexity reduction uses concepts of pruning— removing unnecessary parts of the model that contribute minimally to the final decision, quantization—reducing the precision of weights and activations for efficient com- puting, and knowledge distillation—where the knowledge is transferred to a simpler model that still retains predic- tive capabilities. This reduced model can then be used within a ZKML framework to perform computations effi- ciently while providing privacy guarantees through ZKPs. Furthermore, a stretch goal was to determine the tech- nical feasibility of using the synthetic data generated for ZKML applications effectively in proof and verification systems, given the potential for cross-learning insights without losing privacy. Model Complexity Reduction for the ICHOM Diabetes Synthetic Dataset Model complexity reduction in ZKML is essential to mit- igate overfitting, improve interpretability, and increase computational efficiencies, reducing the computational resources for training and inference. The ZKML software and its model reduction library used for this research was GIZA ZK Cook. The complexity reduction algorithm executes the following steps. 1. Correlation analysis and feature importance for fea- ture selection and reduction: Features with high correlation are candidates that may contribute to redundancy and reduction. Using techniques like recursive feature elimination, features of low impor- tance are eliminated. 2. L1 (Lasso) regularization drives less important fea- ture coefficients to zero, and L2 (Ridge) regularization penalizes large coefficients to reduce complexity without eliminating features. L1 and L2 regularization are com- bined to balance the benefits of both. For tree-based mod- els, pruning removes branches that contribute minimally to the predictions and consolidates and splits nodes. 3. Principal component analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) are applied for dimensionality reduction. 4. Multi-pass cross-validation and hyperparameter tuning for balancing model complexity and accuracy. The MCR was used from the Giza library. The analysis model runs in a Python environment. Results and Discussion The data calibration from the evaluation phase shows the feasibility of practical and substantial use of ICHOM v5 datasets in periodic and continuous monitoring to prevent the progression of conditions, reducing the quality of life (Figure 1), how it relates to the quality-of-life scores ( Figure 2), and how adherence can be mapped granularly (Figure 3). From the Guardian Medx clinical evaluation of syn- thetic data produced based on anonymized extraction from earlier aggregated results for the South Indian population, the model is deemed to help significantly in advancing outcome-based and cost-effective remote Figure 1. Diabetes quality-of-life complications. HBA1c: gly- cated hemoglobin. Source: Copyright by the authors https://doi.org/10.30953/bhty.v7.340 Citation: Blockchain in Healthcare Today 2024, 7: 340 - https://doi.org/10.30953/bhty.v7.340 5 (page number not for citation purpose) Complexity reduction for ZKML monitoring care, based on data analysis and regressor models for the data analyzed in phase 1. This is particu- larly the case in HBA1c (glycated hemoglobin) control, frequency of tracking, and relationship to compli- cations and quality of life scores. The co-existence of other chronic diseases was evident from the data. The ICHOM data captures patient-reported outcomes with WHO scores, which showed a negative trend in quality-of-life scores with increasing HBA1C val- ues reported. All adherence metrics also showed the expected relationship with the HBA1c results. The study opens the possibility of alerts for action based on escalation predictions as the remote monitoring feeds come in for at-scale patient management for early interventions. Model Complexity Before Model Reduction The key aspect is to evaluate the model complexity reduc- tion baselines for this synthetic dataset and compare the Figure 3. Effect of adherence (ADHE) on HBA1c (glycated hemoglobin) control. Source: Copyright by the authors Figure 2. International Consortium for Health Outcomes Measurement: WHO (World Health Organization) scores versus HBA1c (glycated hemoglobin). Source: Copyright by the authors https://doi.org/10.30953/bhty.v7.340 Citation: Blockchain in Healthcare Today 2024, 7: 340 - https://doi.org/10.30953/bhty.v7.3406 (page number not for citation purpose) S. Krishnasamy and I. Govindarajan before-and-after model parameter complexity. This is cru- cial to optimizing the run-time cost and seeing how this model fares for use in distributed ML and collaborative systems over distributed ledgers. The LGBM regressor was configured with the param- eters n-estimators: 1,200 and max-depth: 8 (Figure 4). In contrast, the model complexity is reduced to n-estimators: 150 and max-depth: 4, after the model complexity reduc- tion after running through the ZKCook library (Figure 5). Representing this in terms of nodes: • Number of Nodes = Number of trees* (2 depth -1) • The before complexity reduction number of nodes evaluate to 1,200 * (2 8 – 1) = 306,000 • The before complexity reduction number of nodes evaluate to 150 * (2 4 – 1) = 2,250 Model Complexity After Model Complexity Reduction The difference accounts for a 99.26% reduction, and it is also in line with some of the other reference examples. The choice of the regressor for the problem and further reduc- tion using MCR based on the evaluation data shows that the ICHOM V5 diabetes dataset can be used to capture data so that the interoperability can be enhanced to study and report results in collaborative settings to be used in ZKML applications. These results imply that the needed adaptability for the diabetes study could be captured in the global for- mat of ICHOM and show promise based on the eval- uation data. In addition, these results show promise in generating privacy-protected proofs for verification systems and collaborative sharing of proof of diagno- sis based on patient consent in a privacy-protected way with other collaborating parties. Given these results in the evaluation phase, the proofs can be generated optimized for computational efficiency once the study progresses. This specific adaptation included a subset of the complete dictionary of the ICHOM dataset, as it was adapted to this clinical setting, which is a limitation. So, we see this as a starting point for other work to use standardized datasets such as the ICHOM datasets in different cohorts and other diseases from the ICHOM datasets. Some of those situations will have an increased number of data columns and data analysis requirements, which will give us additional reference points for com- plexity before and after baselines and their impact on proof and verification systems. Also, an important point to note is that this is a preliminary baseline, as the tech- nology matures very fast from all angles—standards development, model reduction techniques, reduction of proof, and verification systems acceleration both at the software and the hardware level. Hence, it will become Figure 4 Model complexity before model complexity reduction. ICHOM: International Consortium for Health Outcomes Measurement; MCR: model complexity reducer. Source: Copyright by the authors https://doi.org/10.30953/bhty.v7.340 Citation: Blockchain in Healthcare Today 2024, 7: 340 - https://doi.org/10.30953/bhty.v7.340 7 (page number not for citation purpose) Complexity reduction for ZKML important to have a registry of ZKML developments across these parameters. Conclusions and Future Work Based on data analysis and regressor models for the data analyzed in the evaluation phase, GuardianMedx clinical evaluation deems the model to help significantly advance outcome-based and cost-effective remote monitoring care. Applying the Giza ZKcook model complexity reduction algorithm to ICHOM diabetes data resulted in more interpretable and computationally efficient models. The computing times were significantly reduced on a standardized ICHOM dataset to use ML and privacy-protected settings to retain data at the source. This increases security and provides verifiable proofs for any prediction models driving agents and to use ML models in conjunction with decentralized distributed led- gers to open collaboration possibilities without giving out the internal details of the data. Given these results in the evaluation phase, once the study progresses, the proofs can be generated optimized for computational efficiency. Further work can be extended to other adaptations of the ICHOM framework for diabetes in another cohort in another setting to compare results, as well as using them in other disease datasets from ICHOM. Shortly, the team intends to extend the ZKML functionalities to feed agents for further processing and advancing priva- cy-protected multi-party insights. Funding None. Conflicts of Interest None. Contributors Sathya Krishnasamy is the President and Principal of ChainAim Technologies. His 25 years of background span extensive experience in managed care payor settings in leading U.S. healthcare firms, including Aetna and Anthem. He focuses on emerging technologies, includ- ing AI/ML systems and distributed ledger technologies. He also serves as an advisor in many industry efforts in payor-provider collaboration, standards organizations, and efforts such as Account Aggregators in India advanc- ing Fintech, Healthcare, and Skills sectors. He currently serves as President and Principal at ChainAim, offering technical strategy consulting and application and devel- opment services. Sathya Krishnasamy helped conceptualize the use of ICHOM for data, evaluate the synthetic dataset, estab- lish a model complexity baseline, and evaluate the ZKML healthcare use case. Dr Govindarajan is the Chief Medical Officer of GuardianMedX. He is a healthcare executive with a Strong Medical Background and technological knowl- edge. He has 35 years of extensive experience in internal Figure 5. Model complexity before model complexity reduction. ICHOM: International Consortium for Health Outcomes Measurement; MCR: model complexity reducer. Source: Copyright by the authors https://doi.org/10.30953/bhty.v7.340 Citation: Blockchain in Healthcare Today 2024, 7: 340 - https://doi.org/10.30953/bhty.v7.3408 (page number not for citation purpose) S. Krishnasamy and I. Govindarajan medicine and geriatrics in India and serves as an advi- sor for geriatric and palliative care for many government entities in India. He has managed and administered patient-centric quality care following a unique con- tinuum of care in clinics, hospitals, nursing homes, hospices, and homes. Dr Govindarajan started the initiative and performed the research for data collection needs, design and evalua- tion of the ICHOM for diabetes data, and clinical evalua- tion of the synthetic datasets. Data Availability Statement (DAS), Data Sharing, Reproducibility, and Data Repositories The data dictionary for the ICHOM V5 diabetes data dictionary is available at https://www.ichom.org/ patient-centered-outcome-measure/diabetes/ Application of AI-Generated Text or Related Technology None. 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BMC Health Serv Res. 2022 Nov 28;22(1):1424. https://doi.org/10.1186/ s12913-022-08694-9 APPENDIX Acronyms defined AI/ML: Artificial Intelligence / Machine Learning HBA1c: Glycoxylated Hemoglobin ICHOM: International Consortium for Health Outcomes Measurement LightGBM: Light Gradient Boosting Machine MCR: Model Complexity Reducer ML: Machine Learning zk-SNARKs: Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge ZKML: Zero knowledge machine learning ZKP: Zero-knowledge proofs Copyright Ownership: This is an open-access article distributed in accordance with the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, and the use is non-commercial. See http://creativecommons. org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v7.340 https://www.ichom.org/patient-centered-outcome-measure/diabetes/ https://www.ichom.org/patient-centered-outcome-measure/diabetes/ https://link.springer.com/chapter/10.1007%2F3-540-47721-7_12 https://eprint.iacr.org/2013/879.pdf https://eprint.iacr.org/2013/879.pdf https://eprint.iacr.org/2018/046 https://arxiv.org/abs/2303.15563 https://github.com/gizatechxyz/zkcook/blob/main/docs/mcr.pdf https://github.com/gizatechxyz/zkcook/blob/main/docs/mcr.pdf https://doi.org/10.1016/j.heliyon.2022.e10904 https://doi.org/10.1016/j.heliyon.2022.e10904 https://doi.org/10.2196/16922 https://www.ichom.org/patient-centered-outcome-measure/diabetes/ https://www.ichom.org/patient-centered-outcome-measure/diabetes/ https://doi.org/10.1186/s12913-022-08694-9 https://doi.org/10.1186/s12913-022-08694-9 http://creativecommons. org/licenses/by-nc/4.0 http://creativecommons. org/licenses/by-nc/4.0