MediVault: A Privacy-preserving Data Collaboration Platform for Healthcare

Healthcare organisations and academic research institutions generate massive volumes of patient datasets that are hold the key to improving diagnoses, tailoring treatments, and enhancing healthcare services, yet stringent privacy regulations often limit their capacity to collaborate effectively. This project proposes a novel framework for secure data collaboration in healthcare, employing cutting-edge cryptographic methods and machine learning techniques to address these concerns. At the core of our platform are three core components: **Secure Multiparty Computation (SMPC), Homomorphic Encryption (HE), and Federated Learning (FL)**. SMPC enables multiple stakeholders, e.g., hospitals, clinics, and research institutes, to perform collective computations on encrypted datasets. In this setup, no single entity gains access to another party's underlying data, thus preserving patient confidentiality. Homomorphic Encryption extends this protection by allowing mathematical operations to be conducted on encrypted data without the need for decryption, enhancing both privacy and computational accuracy. Meanwhile, Federated Learning provides a distributed mechanism to enable hospitals, clinics, and research initiations to train machine learning models locally at each participating institution, sharing only model parameters rather than raw data. These techniques are supported by peer-reviewed cryptographic protocols and represent state-of-the-art approaches in privacy-preserving data analytics. By ensuring that sensitive patient information remains within each organisation's secure domain, the platform addresses prevailing regulatory requirements, including the General Data Protection Regulation (GDPR). Our method minimizes the risk of unauthorised data exposure, fostering greater trust among stakeholders and, ultimately, with the public. The project not only advances the theoretical foundations of privacy-preserving computation but also contributes to the practical integration of these techniques in real-world healthcare applications, such as early disease detection, patient risk stratification, and multi-institutional clinical research. By facilitating anonymised data sharing and collaborative analysis, the platform holds potential to accelerate medical insights while upholding rigorous ethical and legal standards. By enabling responsible data sharing and robust collaboration, this project paves the way for the next generation of data-driven healthcare. In the future, our platform could be adapted to other sectors, but our initial focus remains squarely on healthcare, where privacy-preserving approaches are critically needed. We believe that this technology has the potential to accelerate medical breakthroughs, enhance clinical research, and foster greater trust in digital health systems worldwide.