Towards Post-Quantum Secure Pharmacovigilance with ML-KEM and ML-DSA
2026-06-08 • Cryptography and Security
Cryptography and Security
AI summaryⓘ
The authors created a simple example system to show how future-proof encryption methods can protect sensitive healthcare data from powerful quantum computers. Their system encrypts and signs files like reports and observations so they stay safe and unchanged. They tested the system with fake healthcare data and found the new quantum-safe methods add only a small time delay compared to regular encryption. This work helps understand how to build secure data systems for healthcare once quantum computers become more common, but it is not yet ready for real-world use.
pharmacovigilancepost-quantum cryptographyML-KEM-768AES-256-GCMdigital signatureskey establishmentHKDF-SHA-256ML-DSA-65quantum computingdata encryption
Authors
Saee Desai, Tom Shimoni, Eddie Cameron, David Akamine, Aniketh Chunduri
Abstract
Pharmacovigilance systems handle sensitive healthcare and drug-safety data, including adverse event reports and clinical observations. As quantum computing advances, classical public-key cryptographic systems such as RSA and elliptic-curve cryptography may become vulnerable, creating long-term risks for healthcare data that must remain confidential for many years. This paper presents an educational prototype of a post-quantum secure pharmacovigilance data pipeline. The system uses ML-KEM-768 for post-quantum key establishment, HKDF-SHA-256 for deriving an AES key, AES-256-GCM for efficient file encryption, and ML-DSA-65 for digital signatures and tamper detection. The pipeline supports multiple file formats, including TXT, CSV, JSON, and PDF, by treating files as raw bytes and preserving metadata for reconstruction at the receiver. The prototype includes separate hospital, gateway, pharma receiver, attacker, benchmarking, and dashboard components. We evaluate the system using synthetic pharmacovigilance datasets of different sizes and formats. Our results show that ML-KEM adds a small constant overhead, while AES encryption and ML-DSA signing dominate runtime as file size increases. This work is not a production-ready healthcare system, but rather an educational systems-level exploration of how post-quantum cryptographic primitives can be integrated into healthcare-style data pipelines.