Scalable Private Set Intersection Based on OT Extension

By Benny Pinkas, Thomas Schneider, Michael Zohner. 2018

394 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

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”
PIR with Compressed Queries and Amortized Query Processing (2018, background): “However, several works [24, 65] have estimated this probability empirically for different parameter configurations.”
Fast Private Set Intersection from Homomorphic Encryption (2017, methodology): “While our protocol can easily handle symmetric set sizes, our main advantage over [50] is in the asymmetric setting, which we now focus on.”
Vertical Federated Learning: Concepts, Advances, and Challenges (2022, background): “PSI protocols can be realized using various techniques, such as encryption and signature strategies [96] and oblivious transfer [97, 98] etc.”
Practical Multi-party Private Set Intersection from Symmetric-Key Techniques (2017, methodology): “Current state-of-the-art semi-honest PSI protocols in the twoparty setting are [26, 40].”
Labeled PSI from Fully Homomorphic Encryption with Malicious Security (2018, methodology): “In particular, we follow the PSZ paradigm [36, 43, 45, 46], where the OPRF is applied to the items after cuckoo hashing.”
Private Join and Compute from PIR with Default (2020, background): “…a negligible function negl ( · ) such that The above security deﬁnition formalizes the intuition that the client does not learn anything more than the output of its query (the actual value if the item is present, or the default value) and the database size, and the server does not learn…”