论文标题
对当地差异隐私的强大优化
Robust Optimization for Local Differential Privacy
论文作者
论文摘要
我们考虑发布数据的设置而不会泄漏敏感信息。我们在强大的当地差异隐私（RLDP）的框架内这样做。这确保了不确定性集中所有数据分布的隐私。我们制定了找到最佳数据发布协议作为强大优化问题的问题。通过得出所涉及的约束双重表达式的封闭形式表达式，我们获得了凸优化问题。我们比较了四个可能的优化问题的性能，具体取决于我们是否需要在i）实用程序和ii）隐私方面进行比较。
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.
