Birds of a Feather Flock Together: How Set Bias Helps to Deanonymize You via Revealed Intersection Sizes
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Presented at USENIX Security 2022 by
Secure two-party protocols that compute intersection-related statistics have attracted much attention from the industry. These protocols enable two organizations to jointly compute a function (e.g., count and sum) over the intersection of their sets without explicitly revealing this intersection. However, most of such protocols will reveal the intersection size of the two sets in the end. In this work, we are interested in how well an attacker can leverage the revealed intersection sizes to infer some elements' membership of one organization's set. Even disclosing an element's membership of one organization's set to the other organization may violate privacy regulations (e.g., GDPR) since such an element is usually used to identify a person between two organizations. We are the first to study this