In this work, we study ad conversion measurement, a central functionality in digital advertising, where an advertiser seeks to estimate advertiser website (or mobile app) conversions attributed to ad impressions that users have interacted with on various publisher websites (or mobile apps). Using differential privacy (DP), a notion that has gained in popularity due to its strong mathematical guarantees, we develop a formal framework for private ad conversion measurement. In particular, we define the notion of an operationally valid configuration of the attribution rule, DP adjacency relation, contribution bounding scope and enforcement point. We then provide, for the set of configurations that most commonly arises in practice, a complete characterization, which uncovers a delicate interplay between attribution and privacy.

翻译：本文研究了广告转化测量这一数字广告中的核心功能，即广告主试图估算归因于用户在各发布商网站（或移动应用）上互动过的广告印象的广告主网站（或移动应用）转化。利用差分隐私（DP）这一因其强大的数学保证而日益流行的概念，我们构建了一个用于隐私保护广告转化测量的正式框架。具体而言，我们定义了归因规则、DP邻接关系、贡献限定范围和执行点的操作性有效配置概念。随后，我们针对实践中最常见的一组配置提供了完整刻画，揭示了归因与隐私之间微妙的相互作用。