The analysis of the privacy properties of Privacy-Preserving Ads APIs is an area of research that has received strong interest from academics, industry, and regulators.Despite this interest, the empirical study of these methods is severely hindered by the lack of publicly available data.Reliable empirical analysis of the privacy properties of an API, in fact, requires access to a dataset consisting of realistic API outputs for a large collection of users; however, privacy concerns prevent the general release of such data to the public.In this work, we address this problem by developing a novel methodology to construct synthetic API outputs that are simultaneously realistic enough to enable accurate study and provide strong privacy protections.We focus on one of the Privacy-Preserving Ads APIs: the Topics API, part of Google Chrome's Privacy Sandbox, which enables interest-based advertising without relying on third-party cookies.We developed a methodology to generate a differentially-private dataset that closely matches the re-identification risk properties of the real Topics API data.The use of differential privacy provides strong theoretical bounds on the leakage of private user information from this release.Our methodology is based on first computing a large number of differentially-private statistics describing how output API traces evolve over time.Then, we design a parameterized distribution over sequences of API traces and optimize its parameters so that they closely match the statistics obtained.Finally, we create the synthetic data by drawing from this distribution.Our work is complemented by an open-source release of the anonymized dataset obtained by this methodology.We hope this will enable external researchers to analyze the API in-depth and replicate prior and future work on a realistic large-scale dataset.

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.

The Internet's interdomain routing protocol, BGP, is vulnerable to a number of damaging attacks, which often arise from operator misconfiguration. Proposed solutions with strong guarantees require a public-key infrastructure, accurate routing registries, and changes to BGP. However, BGP routers can avoid selecting and propagating these routes if they are cautious about adopting new reachability information. We describe a protocol- preserving enhancement to BGP, Pretty Good BGP (PGBGP), that slows the dissemination of bogus routes, providing network operators time to respond before problems escalate into large- scale Internet attacks. Simulation results show that realistic deployments of PGBGP could provide 99% of Autonomous Systems with 24 hours to investigate and repair bogus routes without affecting prefix reachability. We also show that without PGBGP, 40% of ASs cannot avoid selecting bogus routes; with PGBGP, this number drops to less than 1%. Finally, we show that PGBGP is incrementally deployable and offers significant security benefits to early adopters and their customers.