Application of ZKML for Unpredictive Epidemic Response

Authors

  • Jin Ah Seo Sogang University, Department of Computer Science, Seoul, South Korea
  • Kun Hwa Lee Seoul National University, Department of Electrical and Computer Engineering, Seoul, South Korea
  • Vijayan Sugumaran Institute for Data Science, School of Business Administration, Oakland University, Rochester, Michigan, USA
  • Jo Yeon Park Sogang University, Department of Computer Science, Seoul, South Korea
  • Soo Yong Park Sogang University, Department of Computer Science, Seoul, South Korea

DOI:

https://doi.org/10.13052/jwe1540-9589.2556

Keywords:

Zero-knowledge proof, machine learning, zero-knowledge machine learning, privacy, medical

Abstract

We build and evaluate a concrete Zero-Knowledge Machine Learning (ZKML)-based pipeline for epidemic diagnosis and show that it can enforce computational integrity without exposing raw medical data in a Web3 setting. In response to security challenges posed by centralized data handling in medical AI applications, particularly during public health crises such as COVID-19, ZKML offers a privacy-preserving alternative by combining machine learning and Zero-Knowledge Proofs (ZKP). We experimentally applied ZKML to a CNN (Convolutional Neural Networks)-based COVID-19 diagnostic model, achieving 87% accuracy and 0.35 loss. All proof generation and verification processes were executed entirely off-chain, with the verified outputs represented as committed public_vals recorded on-chain via smart contracts. To ensure authenticity, the system enforces dual ECDSA signature verification from both the model provider and the data provider. This mechanism prevents unauthorized submissions and confirms the validity of the result before it is stored on-chain. The system was tested under both normal and adversarial conditions, demonstrating robust and reliable operation. By enabling decentralized trust and self-sovereign control over data, this architecture aligns well with Web3 principles. The results indicate that ZKML can support the development of privacy-preserving and verifiable AI systems.

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Author Biographies

Jin Ah Seo, Sogang University, Department of Computer Science, Seoul, South Korea

Jin Ah Seo is a sixth-semester Ph.D. student in the Department of Computer Science at Sogang University. She received her master’s degree from the Graduate School of Information and Communication at Sogang University in 2022. Her research interests include blockchain security, with a current focus on Zero-Knowledge Machine Learning (ZKML) and AI security requirements.

Kun Hwa Lee, Seoul National University, Department of Electrical and Computer Engineering, Seoul, South Korea

Kun Hwa Lee is a second-semester master’s degree student in the Department of Electrical and Computer Engineering at Seoul National University. He received his bachelor’s degree in Computer Science Engineering from Sogang University in 2025. His research interests are in Machine Learning Security and Privacy. He is currently working on Model IP Protection in Federated Learning.

Vijayan Sugumaran, Institute for Data Science, School of Business Administration, Oakland University, Rochester, Michigan, USA

Vijayan Sugumaran is a Distinguished University Professor and Janke Scholar of Management Information Systems in the School of Business Administration at Oakland University, Rochester, Michigan, USA. He is also the Chair of the Department of Decision and Information Sciences, Co-Director of the Institute for Data Science, and Director of the Master of Science in Business Analytics program. He received his Ph.D. in Information Technology from George Mason University, Fairfax, Virginia, USA. His research interests are in the areas of Big Data Management and Analytics, Ontologies and Semantic Web, Intelligent Agent and Multi-Agent Systems. Sugumaran is the Co-PI on a US$2 million NSF grant to train students in STEM-driven data science and entrepreneurship. He has published over 350 peer-reviewed articles in journals, conferences, and books. He has edited 20 books and serves on the Editorial Board of eight journals. He has published in top-tier journals such as Information Systems Research, ACM Transactions on Database Systems, Communications of the ACM, IEEE Transactions on Big Data, IEEE Transactions on Engineering Management, IEEE Transactions on Education, IEEE Transactions on Cybernetics, IEEE Multimedia, and IEEE Software. Sugumaran is the editor-in-chief of the International Journal of Intelligent Information Technologies and Journal of Web Engineering. He is the Chair of the Intelligent Agent and Multi-Agent Systems mini-track for Americas Conference on Information Systems (AMCIS 1999–2025). Sugumaran has served as the Program Chair for the 14th Workshop on E-Business (WeB2015), the International Conference on Applications of Natural Language to Information Systems (NLDB 2008, NLDB 2013, NLDB 2016, NLDB 2019, NLDB 2023, and NLDB 2024), 29th Australasian Conference on Information Systems (ACIS 2018), 14th Annual Conference of Midwest Association for Information Systems (MWAIS 2019), 5th IEEE International Conference on Big Data Service and Applications (BDS 2019), and 2022 Midwest Decision Sciences Institute Annual Conference (MWDSI 2022). He also regularly serves as a program committee member for numerous national and international conferences.

Jo Yeon Park, Sogang University, Department of Computer Science, Seoul, South Korea

Jo Yeon Park graduated in 2026 from the Department of Computer Science at Sogang University. She received her bachelor’s degree in Intellectual Property from Kyonggi University in 2023. Her research interests focus on the convergence of blockchain technology and intellectual property, and she has worked on Zero-Knowledge Machine Learning (ZKML) technologies.

Soo Yong Park, Sogang University, Department of Computer Science, Seoul, South Korea

Soo Yong Park received his Ph.D. degree from George Mason University in 1995. He has held several prestigious positions, including serving as a Professor in the Department of Computer Science at Sogang University since March 1998 and as the Dean of the College of Software Convergence at Sogang University since July 2024. He is currently serving as the Chair of the Distributed Ledger Standard Forum and has been the Director of the Web 3.0 Research Center (ITRC) since 2023. Previously, he served as the President and CEO of the National IT Industry Promotion Agency (NIPA) from September 2012 to November 2014. Since January 2019, he has also served as the President of the Korea Society of Blockchain and as the Director of the Intelligent Blockchain Research Center. His accolades include the 10-Year Most Influential Paper Award from the Asia-Pacific Software Engineering Conference (APSEC) in December 2018 and the Minister of Science and ICT Award for Best Project Evaluation in November 2021.

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Published

2026-07-06

How to Cite

Seo, J. A. ., Lee, K. H. ., Sugumaran, V. ., Park, J. Y. ., & Park, S. Y. . (2026). Application of ZKML for Unpredictive Epidemic Response. Journal of Web Engineering, 25(05), 889–914. https://doi.org/10.13052/jwe1540-9589.2556

Issue

2026: Vol 25 Iss 5

Section

Articles