Computationally Secure Oblivious Transfer

By Moni Naor, Benny Pinkas. 2004

207 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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In Computational Theory and Mathematics

Citations in the corpus

I-Pic: A Platform for Privacy-Compliant Image Capture (2016, methodology): “Delivering the Bystander Agent’s inputs to the garbled circuit requires a Diffie-Hellman key exchange (DH) and two rounds of oblivious transfers (NPOT [22] and OTEXT [23]), which are partly piggy-backed on the secure dot product protocol messages, and shown in Figure 4 (Messages 4, 5, 6 and 9).”
DeepSecure: Scalable Provably-Secure Deep Learning (2017, background): “In this setting, R wants to receive xr among the sender’s messages without the sender learning the index r , while the receiver only obtains one of the n possible messages [8].”
Efficient Privacy-Preserving Biometric Identification (2011, methodology): “As our base OT we use the Naor-Pinkas protocol [14] which achieves semi-honest security based on the decisional Diffie-Hellman assumption.”
Faster Secure Two-Party Computation Using Garbled Circuits (2011, methodology): “In our implementation we use the Naor-Pinkas protocol [24], secure in the semi-honest setting.”
Quantum And Relativistic Protocols For Secure Multi-Party Computation (2009, background): “Since oblivious transfer protocols based on computational assumptions exist (see for example [35]), we can generate technologically secure protocols for two-party secure computations in the classical world.”
Compacting privacy-preserving k-nearest neighbor search using logic synthesis (2015, methodology): “, xn); R provides a selection number r with 1 ≤ r ≤ n and obtains xr as the output [25].”