Blockchain-Based Random Selection Among a Private Number of Candidates

Random selection processes are fundamental across diverse domains, requiring transparency and fairness in execution. However, some contexts, such as sensitive ecosystems like healthcare or education, also require an additional degree of privacy by avoiding information leakage about the number of candidates for the selection. Despite this, existing literature lacks comprehensive solutions that maintain this privacy while upholding transparency and fairness. To address this gap, the study proposes a platform that combines blockchain and Homomorphic Encryption. The main contributions include a detailed definition of the architecture and the operational procedures, and an in-depth exploration of the proposed Random Number Generator function. The study introduces a homomorphic approach for confidentially computing candidate counts and explores methods for homomorphically constraining random numbers within a private range. By combining blockchain and Homomorphic Encryption, this research offers a robust solution for transparent and fair random selection while maintaining the number of candidates private.