Development of Efficient Homomorphic Secret Sharing Schemes for Applications in Public Health

Context & Background

Collaborative research in public health requires sharing sensitive patient data (like clinical outcomes, genomic profiles). However, strict privacy laws (like HIPAA and GDPR) prohibit sharing raw medical data. Homomorphic encryption and secret sharing make it possible to perform computations on encrypted data directly, preserving absolute privacy.

Problems to be Addressed

Traditional homomorphic encryption schemes are computationally heavy and require large bandwidth, making them impossible to run on low-power mobile or IoT devices used by health workers in the field.

Aims and Objectives

1. Develop efficient homomorphic secret sharing (HSS) schemes.
2. Apply these schemes to secure public health data collection.
3. Optimize algorithms to run on resource-constrained devices.

Methodology

The project uses algebraic coding theory and error-correcting codes to formulate secret sharing protocols. By dividing secrets into shares that cannot be deciphered individually, the system performs secure distributed computation. The algorithms are tested on mobile device simulators to measure performance and energy footprint.

Expected Outcomes

Highly secure data sharing protocols, software implementation modules for mobile health apps, and training of postdoctoral researchers.