We consider the setting of publishing data without leaking sensitive information. We do so in the framework of Robust Local Differential Privacy (RLDP). This ensures privacy for all distributions of the data in an uncertainty set. We formulate the problem of finding the optimal data release protocol as a robust optimization problem. By deriving closed-form expressions for the duals of the constraints involved we obtain a convex optimization problem. We compare the performance of four possible optimization problems depending on whether or not we require robustness in i) utility and ii) privacy.

翻译：我们考虑在不泄露敏感信息的情况下设置公布数据,我们在“地方差异隐私”的框架内这样做。这确保了在不确定因素组中所有数据分发的隐私。我们把找到最佳数据发布协议的问题当作一个强大的优化问题。通过对所涉限制的双重性进行闭式表达,我们得到了一个曲线优化问题。我们根据是否要求在i) 效用和ii) 隐私中是否强健,比较了四个可能的优化问题的表现。