论文标题
在当地差异隐私下的线性查询的工作负载自适应机制
A workload-adaptive mechanism for linear queries under local differential privacy
论文作者
论文摘要
我们提出了一种新的机制，以准确回答局部微分隐私（LDP）下的用户提供的线性计数查询集。给定一组线性计数查询（工作负载）我们的机制会自动适应工作负载查询的准确性。我们定义了一类参数类别的机制，这些机制产生了工作量的无偏估计，并制定了约束优化问题，以从此类中选择一种最小化预期总平方误差的机制。我们使用投影梯度下降来数字地解决此优化问题，并提供有效的实现，以扩展到大型工作负载。我们在各种环境中证明了基于优化的方法的有效性，这表明它表现优于许多竞争对手，甚至超过了预期的工作负载上的现有机制。
We propose a new mechanism to accurately answer a user-provided set of linear counting queries under local differential privacy (LDP). Given a set of linear counting queries (the workload) our mechanism automatically adapts to provide accuracy on the workload queries. We define a parametric class of mechanisms that produce unbiased estimates of the workload, and formulate a constrained optimization problem to select a mechanism from this class that minimizes expected total squared error. We solve this optimization problem numerically using projected gradient descent and provide an efficient implementation that scales to large workloads. We demonstrate the effectiveness of our optimization-based approach in a wide variety of settings, showing that it outperforms many competitors, even outperforming existing mechanisms on the workloads for which they were intended.
