Predictive metric for optimal budget allocation in differential privacy - Nunes, Henry Cabral (2024)
This work addresses the critical issue of budget allocation in Differential Privacy (DP) applications, specifically for scenarios where summary statistics are released. Our main objective is to develop a novel metric and scenario that leverages information about future data usage to optimize budget distribution. Effective budget distribution is pivotal in enhancing data utility without compromising privacy, a significant challenge in the DP field. We identify and exploit a gap related to the interactions between DP queries to improve data utility. Our metric is formally defined, and we apply it through a hypothetical scenario using synthetic data. The results indicate a substantial improvement in data utility while maintaining privacy. This study offers a valuable contribution to the DP field and opens avenues for future research and practical applications in real-world scenarios