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SIGMOD
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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