Fast Private Set Intersection from Homomorphic Encryption

By Hao Chen, Kim Laine, Peter Rindal. 2017

461 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

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.”
Simple Encrypted Arithmetic Library v2.3.0 (2017, background): “For a concrete example, we refer the reader to [14].”
Labeled PSI from Fully Homomorphic Encryption with Malicious Security (2018, methodology): “• We use an OPRF preprocessing phase to achieve improved performance and security over [12].”
Scalable Private Set Union from Symmetric-Key Techniques (2019, methodology): “Over the last decade, there has been a signiﬁcant amount of work on private set intersection [15, 16, 3, 29, 18, 46, 47, 36, 12, 37, 51, 11, 27, 49, 10, 20, 13, 17, 45, 43, 32].”
A Survey of COVID-19 Contact Tracing Apps (2020, background): “s attacks discussed in Section 5. At the time of writing, the source code has not been made available. At the heart of the protocol is a cryptographic technique known as Private Set Intersection (PSI)[23, 66]. At a high level, devices generate and share random IDs (tokens) using a seed. Each device ”
On Deploying Secure Computing Commercially: Private Intersection-Sum Protocols and their Business Applications (2019, background): “[10] leverage fullyhomomorphic encryption to create PSI protocols which offer improved communication efficiency, while at the same time allowing retrieval of associated data”
Communication-Computation Trade-offs in PIR (2019, methodology): “This functionality has been used in the context of private set intersection protocols [13], but it can also be used as a first step in private join and compute protocols [59] which enable restricted computation over the join of two private databases.”
Simple Encrypted Arithmetic Library - SEAL v2.1 (2016, background): “For a concrete example, we refer the reader to [13].”