Do I know you?: efficient and privacy-preserving common friend-finder protocols and applications

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How researchers see this paper

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Secure Federated Submodel Learning (2019, background): “Compared with Private Set Intersection (PSI) [71]–[76] and Private Set union Cardinality (PSC) [77]– [79], which have received tremendous attention and also have seen several practical applications, such as in social networks [80], [81]; human genome testing [82]; locationbased services [83]; securi”
Faster Private Set Intersection Based on OT Extension (2014, unknown): “(When inputs to PSI have a high entropy, a protocol that compares hashes of the inputs can be used [46].)”
SDDR: Light-Weight, Secure Mobile Encounters (2014, background): “While their solution provides ample efficiency gains over standard PSI, saving an order of magnitude in communication and computation costs, several seconds per interactive exchange is too much when running on power-constrained devices in dense environments.”
Scalable Private Set Intersection Based on OT Extension (2018, methodology): “(When inputs to PSI have high entropy, a protocol that compares hashes of the inputs can be used [Nagy et al. 2013].”
Labeled PSI from Fully Homomorphic Encryption with Malicious Security (2018, unknown): “[39] considers a private friend-finding scenario.”