@PHDTHESIS{ 2024:310635451,
 	title = {Predictive metric for optimal budget allocation in differential privacy},
 	year = {2024},
 	url = "https://tede2.pucrs.br/tede2/handle/tede/11680",
 	abstract = "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",
 	publisher = {Pontif?cia Universidade Cat?lica do Rio Grande do Sul},
 	scholl = {Programa de P?s-Gradua??o em Ci?ncia da Computa??o},
 	note = {Escola Polit?cnica}
}