Differential privacy has become a popular privacy-preserving method in data analysis, query processing, and machine learning, which adds noise to the query result to avoid leaking privacy. Sensitivity, or the maximum impact of deleting or inserting a tuple on query results, determines the amount of noise added. Computing the sensitivity of some simple queries such as counting query is easy, however, computing the sensitivity of complex queries containing join operations is challenging. Global sensitivity of such a query is unboundedly large, which corrupts the accuracy of the query answer. Elastic sensitivity and residual sensitivity offer upper bounds of local sensitivity to reduce the noise, but they suffer from either low accuracy or high computational overhead. We propose two fast query sensitivity estimation methods based on sampling and sketch respectively, offering competitive accuracy and higher efficiency compared to the state-of-the-art methods.

翻译：差分隐私已成为数据分析、查询处理和机器学习中流行的隐私保护方法，它通过向查询结果添加噪声来避免隐私泄露。敏感度，即删除或插入一条元组对查询结果的最大影响，决定了添加噪声的强度。计算计数查询等简单查询的敏感度较为容易，然而，对于包含连接操作的复杂查询，其敏感度计算具有挑战性。此类查询的全局敏感度可能无限大，从而影响查询答案的准确性。弹性敏感度和残差敏感度提供了局部敏感度的上界以减少噪声，但它们存在准确性低或计算开销高的问题。我们提出了两种基于采样和草图技术的快速查询敏感度估计方法，与现有最先进方法相比，在保持竞争性准确性的同时，实现了更高的效率。