In this work, we give a new technique for analyzing individualized privacy accounting via the following simple observation: if an algorithm is one-sided add-DP, then its subsampled variant satisfies two-sided DP. From this, we obtain several improved algorithms for private combinatorial optimization problems, including decomposable submodular maximization and set cover. Our error guarantees are asymptotically tight and our algorithm satisfies pure-DP while previously known algorithms (Gupta et al., 2010; Chaturvedi et al., 2021) are approximate-DP. We also show an application of our technique beyond combinatorial optimization by giving a pure-DP algorithm for the shifting heavy hitter problem in a stream; previously, only an approximateDP algorithm was known (Kaplan et al., 2021; Cohen & Lyu, 2023).

翻译：在本工作中，我们提出了一种分析个性化隐私核算的新技术，其核心基于以下简单观察：若某算法满足单侧加性差分隐私，则其子采样变体满足双侧差分隐私。基于此，我们为若干私有组合优化问题提供了改进算法，包括可分解子模最大化和集合覆盖问题。我们的误差保证是渐近紧的，且算法满足纯差分隐私，而先前已知算法（Gupta等人，2010；Chaturvedi等人，2021）仅满足近似差分隐私。我们还展示了该技术在组合优化之外的应用，为流式数据中的偏移频繁项检测问题提供了纯差分隐私算法；此前仅有近似差分隐私算法被提出（Kaplan等人，2021；Cohen与Lyu，2023）。