We study the problem of counting the number of distinct elements in a dataset subject to the constraint of differential privacy. We consider the challenging setting of person-level DP (a.k.a. user-level DP) where each person may contribute an unbounded number of items and hence the sensitivity is unbounded. Our approach is to compute a bounded-sensitivity version of this query, which reduces to solving a max-flow problem. The sensitivity bound is optimized to balance the noise we must add to privatize the answer against the error of the approximation of the bounded-sensitivity query to the true number of unique elements.

翻译：我们研究在差分隐私约束下统计数据集中唯一元素数量的问题。我们考虑人员级差分隐私（也称用户级差分隐私）这一具有挑战性的场景，其中每个人员可贡献无限数量的条目，因此敏感度无上界。我们的方法是计算该查询的有界敏感度版本，这可以简化为求解最大流问题。通过优化敏感度边界，在添加噪声以隐私化查询结果所需的噪声量，与有界敏感度查询对真实唯一元素数量的近似误差之间取得平衡。