Interactive database systems allow analysts to perform aggregate queries, e.g., statistics, and learn valuable insights about the underlying data population, but these insights might leak sensitive information about individuals in the database. To this end, it is common for database owners to employ privacy-enhancing transformations, e.g., noise injection, to the answers of the queries before sharing them with the analysts. However, such transformations may destroy the utility of the database system, hence, there is a need for appropriate strategies that balance the privacy-utility trade-off. In this project, we will investigate how machine learning agents can assist database owners in identifying optimal strategies for protecting the privacy of statistical queries without harming the data utility. To this end, we will design a multi-agent adversarial learning environment simulating both privacy attackers which aim at maximizing the performance of adversarial attacks on the sensitive data and privacy defenders which aim at protecting the data while preserving its utility, with the overarching goal of uncovering dynamic defense strategies which effectively balance the inherent trade-off between privacy and utility.