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

• URL: http://arxiv.org/abs/2601.12136v1
• Date: Sat, 17 Jan 2026 18:47:17 GMT
• Title: CoSMeTIC: Zero-Knowledge Computational Sparse Merkle Trees with Inclusion-Exclusion Proofs for Clinical Research
• Abstract summary: CoSMeTIC is a zero-knowledge computational framework that generates verifiable inclusion and exclusion proofs for individual participants' data in clinical studies.<n>We demonstrate that CoSMeTIC achieves strong privacy guarantees while maintaining statistical fidelity.<n>Our results suggest that CoSMeTIC provides a scalable and practical alternative for achieving regulatory compliance with rigorous privacy protection in large-scale clinical research.
• Score: 2.0497679299764355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• PSyGenTAB: A Privacy-Preserving Framework for Synthetic Clinical Tabular Data Generation via Constrained Optimization [1.9205944025326402]
  We present PSyGenTAB, a framework for balancing privacy protection and clinical utility in synthetic healthcare data.<n>PSyGenTAB enforces minimum privacy thresholds while maximizing clinical data utility.<n>Downstream evaluation shows that models trained on synthetic data achieve performance comparable to those trained on real patient records.
  arXiv  Detail & Related papers  (2026-06-16T22:14:40Z)
• Equivalence Testing Under Privacy Constraints [0.0]
  Standard equivalence testing procedures for differences in proportions or means can inadvertently disclose sensitive information.<n>We develop differentially private equivalence testing procedures that rely on simulation-based calibration.<n>Our approach introduces a unified framework, termed DP-TOST, for conducting differentially private equivalence testing of both means and proportions.
  arXiv  Detail & Related papers  (2026-04-07T22:06:33Z)
• From Statistical Fidelity to Clinical Consistency: Scalable Generation and Auditing of Synthetic Patient Trajectories [0.8261055975875736]
  We developed a pipeline to make synthetic patient trajectories clinically consistent.<n>We generated 18,071 synthetic patient records from a source of 180,712 real patients.<n>We found no evidence of privacy risks, with membership performance indistinguishable from random guessing.
  arXiv  Detail & Related papers  (2026-03-06T00:17:59Z)
• Responsible Evaluation of AI for Mental Health [72.85175110624736]
  Current approaches to evaluating AI tools in mental health care are fragmented and poorly aligned with clinical practice, social context, and first-hand user experience.<n>This paper argues for a rethinking of responsible evaluation by introducing an interdisciplinary framework that integrates clinical soundness, social context, and equity.
  arXiv  Detail & Related papers  (2026-01-20T12:55:10Z)
• Improving Cardiac Risk Prediction Using Data Generation Techniques [37.94487163156369]
  This work proposes an architecture for the synthesis of realistic clinical records that are coherent with real-world observations.<n>The primary objective is to increase the size and diversity of the available datasets in order to enhance the performance of cardiac risk prediction models.
  arXiv  Detail & Related papers  (2025-12-19T10:17:00Z)
• A Semantic Framework for Patient Digital Twins in Chronic Care [0.0]
  The Patient Medical Digital Twin (PMDT) integrates physiological, psychosocial, behavioral, and genomic information into a coherent model.<n>The PMDT ensures semantic interoperability, supports automated reasoning, and enables reuse across diverse clinical contexts.<n>By bridging gaps in data fragmentation and semantic standardization, the PMDT provides a validated foundation for next-generation digital health ecosystems.
  arXiv  Detail & Related papers  (2025-10-10T08:34:55Z)
• MoodAngels: A Retrieval-augmented Multi-agent Framework for Psychiatry Diagnosis [58.67342568632529]
  MoodAngels is the first specialized multi-agent framework for mood disorder diagnosis.<n>MoodSyn is an open-source dataset of 1,173 synthetic psychiatric cases.
  arXiv  Detail & Related papers  (2025-06-04T09:18:25Z)
• Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models [70.64969663547703]
  AdaCVD is an adaptable CVD risk prediction framework built on large language models extensively fine-tuned on over half a million participants from the UK Biobank.<n>It addresses key clinical challenges across three dimensions: it flexibly incorporates comprehensive yet variable patient information; it seamlessly integrates both structured data and unstructured text; and it rapidly adapts to new patient populations using minimal additional data.
  arXiv  Detail & Related papers  (2025-05-30T14:42:02Z)
• TrialMatchAI: An End-to-End AI-powered Clinical Trial Recommendation System to Streamline Patient-to-Trial Matching [0.0]
  We present TrialMatchAI, an AI-powered recommendation system that automates patient-to-trial matching.<n>Built on fine-tuned, open-source large language models, TrialMatchAI ensures transparency and maintains a lightweight deployment footprint.<n>In real-world validation, 92 percent of oncology patients had at least one relevant trial retrieved within the top 20 recommendations.
  arXiv  Detail & Related papers  (2025-05-13T12:39:06Z)
• Leveraging Generative AI Through Prompt Engineering and Rigorous Validation to Create Comprehensive Synthetic Datasets for AI Training in Healthcare [0.0]
  The GPT-4 API was employed to generate high-quality synthetic datasets aimed at overcoming this limitation.<n>The generated data encompassed a comprehensive array of patient admission information, including healthcare provider details, hospital departments, wards, bed assignments, patient demographics, emergency contacts, vital signs, immunizations, allergies, medical histories, appointments, hospital visits, laboratory tests, diagnoses, treatment plans, medications, clinical notes, visit logs, discharge summaries, and referrals.
  arXiv  Detail & Related papers  (2025-04-29T16:37:34Z)
• Position Paper: Building Trust in Synthetic Data for Clinical AI [0.3937354192623676]
  This paper argues that fostering trust in synthetic medical data is crucial for its clinical adoption.<n>We present empirical evidence from brain tumor segmentation to demonstrate that the quality, diversity, and proportion of synthetic data directly impact trust in clinical AI models.
  arXiv  Detail & Related papers  (2025-02-04T07:53:23Z)
• TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [54.98321887435557]
  This paper presents a suite of 23 meticulously curated AI-ready datasets covering multi-modal input features and 8 crucial prediction challenges in clinical trial design.<n>We provide basic validation methods for each task to ensure the datasets' usability and reliability.<n>We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
  arXiv  Detail & Related papers  (2024-06-30T09:13:10Z)
• TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic Tree-Based Memory Network [54.332862955411656]
  Clinical trials are critical for drug development but often suffer from expensive and inefficient patient recruitment. In recent years, machine learning models have been proposed for speeding up patient recruitment via automatically matching patients with clinical trials. We introduce a dynamic tree-based memory network model named TREEMENT to provide accurate and interpretable patient trial matching.
  arXiv  Detail & Related papers  (2023-07-19T12:35:09Z)
• Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching [49.78442796596806]
  We propose an innovative privacy-aware data augmentation approach for patient-trial matching (LLM-PTM) Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%.
  arXiv  Detail & Related papers  (2023-03-24T03:14:00Z)
• Privacy-preserving medical image analysis [53.4844489668116]
  We present PriMIA, a software framework designed for privacy-preserving machine learning (PPML) in medical imaging. We show significantly better classification performance of a securely aggregated federated learning model compared to human experts on unseen datasets. We empirically evaluate the framework's security against a gradient-based model inversion attack.
  arXiv  Detail & Related papers  (2020-12-10T13:56:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.