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.

本文提出了一个新的理论框架来测量用户表示中的再识别风险，并应用该框架来模拟 Chrome 的 Topics API，以便用于基于兴趣的广告。