Privacy-Preserving Medical Research Platform

A secure medical trial matching system powered by Nillion's secure computation technology

Problem Statement

Medical research requires handling sensitive patient data while maintaining privacy and compliance with healthcare regulations. Traditional systems expose private health information to multiple parties, creating privacy risks and potential HIPAA violations.

Solution

This platform uses Nillion's secure computation to process sensitive medical data while maintaining complete privacy. It enables multi-party computation where different stakeholders (patients, researchers, hospitals) can participate in clinical trials without exposing underlying patient data.

Features

1. Secure Trial Matching

• Age-based eligibility verification
• Symptom pattern matching
• Treatment duration assessment
• Privacy-preserving patient scoring

2. Multi-Party Computation

• Patient data remains encrypted
• Researchers get aggregated insights
• Hospitals maintain oversight
• Zero knowledge of individual records

3. Privacy-Preserving Access Levels

• Patients: Trial eligibility and effectiveness scores
• Researchers: Aggregated trial matches and response scores
• Hospitals: Safety monitoring and trial oversight

Technical Implementation

https://github.com/kamalbuilds/nillion-pvt-medical-research-app?tab=readme-ov-file#technical-implementation

Security Features

1. Data Protection

• End-to-end encryption
• Zero-knowledge computation
• Secure key management

2. Access Control

• Party-specific bindings
• Role-based permissions
• Granular output control

Built With

• nada-dsl
• nillion