Measuring Re-identification Risk

Code

• github.com/google-research/google-research/tree/master/re_identification_risk
  Officialjax★ 0
• github.com/patcg-individual-drafts/topics
  In papernone★ 816
• github.com/jkarlin/topics
  none★ 816

Abstract

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.

Reproductions