Compact user representations (such as embeddings) form the backbone of personalization services. In this work, we present a new theoretical framework to measure re-identification risk in such user representations. Our framework, based on hypothesis testing, formally bounds the probability that an attacker may be able to obtain the identity of a user from their representation. As an application, we show how our framework is general enough to model important real-world applications such as the Chrome's Topics API for interest-based advertising. We complement our theoretical bounds by showing provably good attack algorithms for re-identification that we use to estimate the re-identification risk in the Topics API. We believe this work provides a rigorous and interpretable notion of re-identification risk and a framework to measure it that can be used to inform real-world applications.

翻译：紧凑型用户表示（如嵌入向量）构成了个性化服务的核心。在本文中，我们提出了一个衡量此类用户表示中再识别风险的新理论框架。我们的框架基于假设检验，严格界定了攻击者可能从用户表示中获取其身份的概率。作为应用实例，我们展示了该框架的通用性足以模拟重要的现实场景，例如Chrome的Topics API用于兴趣定向广告。我们通过设计可证明有效的再识别攻击算法来补充理论界限，并利用这些算法估算了Topics API中的再识别风险。我们相信，这项工作为再识别风险提供了严格且可解释的概念，并提供了一套可用于指导实际应用的度量框架。