论文标题
目的：用于差异私有合成数据的自适应和迭代机制
AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data
论文作者
论文摘要
我们提出了AIM，这是一种用于差异私有合成数据生成的新算法。 AIM是在算法范式内首先选择一组查询，然后私下测量这些查询的算法中的工作负载 - 自适应算法，最后从噪声测量中生成合成数据。它使用一组创新功能来迭代选择最有用的测量结果，反映了它们与工作负载的相关性及其在近似输入数据中的价值。我们还提供分析性表达式，以限制界限误差，概率很高，可用于构建置信区间并告知用户生成的数据的准确性。我们从经验上表明，目标始终优于各种实验环境中的各种现有机制。
We propose AIM, a new algorithm for differentially private synthetic data generation. AIM is a workload-adaptive algorithm within the paradigm of algorithms that first selects a set of queries, then privately measures those queries, and finally generates synthetic data from the noisy measurements. It uses a set of innovative features to iteratively select the most useful measurements, reflecting both their relevance to the workload and their value in approximating the input data. We also provide analytic expressions to bound per-query error with high probability which can be used to construct confidence intervals and inform users about the accuracy of generated data. We show empirically that AIM consistently outperforms a wide variety of existing mechanisms across a variety of experimental settings.
