User Representations

A new Framework for Measuring Re-Identification Risk
In this work, we present a new theoretical framework to measure re-identification risk in such user representations. 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.
CJ Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andres Muñoz Medina, Vahab Mirrokni, Gabriel H. Nunes, Sergei Vassilvitskii, Peilin Zhong
Measuring Re-identification Risk
In this work, we present a new theoretical framework to measure re-identification risk in such user representations. 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.
CJ Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andres Muñoz Medina, Vahab Mirrokni, Gabriel H. Nunes, Sergei Vassilvitskii, Peilin Zhong
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Measuring Re-identification Risk
In this work, we present a new theoretical framework to measure re-identification risk in such user representations. 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.
CJ Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andres Muñoz Medina, Vahab Mirrokni, Gabriel H. Nunes, Sergei Vassilvitskii, Peilin Zhong