CoSMeTIC: Zero-Knowledge Computational Sparse Merkle Trees with Inclusion-Exclusion Proofs for Clinical Research

TL;DR

CoSMeTIC introduces a zero-knowledge framework using Sparse Merkle Trees to enable privacy-preserving, verifiable inclusion proofs in clinical data, balancing privacy with regulatory accountability.

Contribution

It proposes a novel zero-knowledge computational approach with Sparse Merkle Trees for verifiable data inclusion in clinical research, ensuring privacy and compliance.

Findings

Achieves strong privacy guarantees with verifiable proofs.

Maintains statistical fidelity in genomic analyses.

Demonstrates scalability and practicality in real-world datasets.

Abstract

Analysis of clinical data is a cornerstone of biomedical research with applications in areas such as genomic testing and response characterization of therapeutic drugs. Maintaining strict privacy controls is essential because such data typically contains personally identifiable health information of patients. At the same time, regulatory compliance often requires study managers to demonstrate the integrity and authenticity of participant data used in analyses. Balancing these competing requirements, privacy preservation and verifiable accountability, remains a critical challenge. In this paper, we present CoSMeTIC, a zero-knowledge computational framework that proposes computational Sparse Merkle Trees (SMTs) as a means to generate verifiable inclusion and exclusion proofs for individual participants' data in clinical studies. We formally analyze the zero-knowledge properties of CoSMeTIC and evaluate its computational efficiency through extensive experiments. Using the Kolmogorov-Smirnov and likelihood-ratio hypothesis tests, along with logistic-regression-based genomic analyses on real-world Huntington's disease datasets, we demonstrate that CoSMeTIC achieves strong privacy guarantees while maintaining statistical fidelity. Our results suggest that CoSMeTIC provides a scalable and practical alternative for achieving regulatory compliance with rigorous privacy protection in large-scale clinical research.