Differentially Private Synthetic Data Release for Topics API Outputs
Abstract
Recently, significant attention has been devoted to the design and analysis of Privacy-Preserving Ads APIs. Despite the interest of academics and regulators in understanding the privacy properties of such APIs, the empirical study of these methods is severely hindered by the lack of publicly-available data. This is because, a reliable empirical analysis of the privacy properties on an API requires access to a dataset consisting of realistic API outputs for a large collection of users. However, privacy reasons prevent the general release of such browsing 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 to the user.
We focus on one of the Privacy-Preserving Ads API: the Topics API; part of Google Chrome's Privacy Sandbox which enables interest-based advertising without relying third-party cookies. We develop a methodology to generate a Differentially Private dataset realistic enough to close match the re-identification risk properties of the real Topics API data. The use of differential privacy prevents the leak 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 matches the statistics obtained. Finally, we create the synthetic data by drawing from this distribution.
Our work is complemented with 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. We believe that this work will contribute to fostering transparency on the privacy properties of Privacy-Preserving Ads APIs.
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 to the user.
We focus on one of the Privacy-Preserving Ads API: the Topics API; part of Google Chrome's Privacy Sandbox which enables interest-based advertising without relying third-party cookies. We develop a methodology to generate a Differentially Private dataset realistic enough to close match the re-identification risk properties of the real Topics API data. The use of differential privacy prevents the leak 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 matches the statistics obtained. Finally, we create the synthetic data by drawing from this distribution.
Our work is complemented with 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. We believe that this work will contribute to fostering transparency on the privacy properties of Privacy-Preserving Ads APIs.
