Extending Oblivious Transfers Efficiently

By Yuval Ishai, Joe Kilian, Kobbi Nissim, Erez Petrank. 2003

853 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Privacy & Security

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

In Privacy & Security

Citations in the corpus

Advances and Open Problems in Federated Learning (2019, background): “Some of the recent advances in MPC can be attributed to breakthroughs in lower level primitives, such as oblivious transfer protocols [211] and encryption schemes with homomorphic properties (as described below).”
QUOTIENT: Two-Party Secure Neural Network Training and Prediction (2019, background): “Using them × COTℓ Extension protocol from [3], the parties running Protocol 7 sendm(τ + ℓ) bits to each other to compute inner products of lengthm, where τ is the security parameter (128 in our implementation).”
Privacy-Preserving Ridge Regression on Hundreds of Millions of Records (2013, methodology): “Third, FastGC implements the OT extension [26] which can execute a practically unlimited number of transfers at the cost of k OTs and several symmetric-key operations per additional OT.”
I-Pic: A Platform for Privacy-Compliant Image Capture (2016, methodology): “The Capture Agent now sends the circuit to the Bystander Agent, along with the garbled values of the obfuscated inputs P ′i,j , and the garbled values of Bystander Agent’s inputs as part of the OTEXT oblivious transfer (Message 9).”
Gazelle: A Low Latency Framework for Secure Neural Network Inference (2018, methodology): “…such as AES and SHA and use them in a clever way [3], because of hardware support in the form of the Intel AES-NI instruction set, and because of techniques such as oblivious transfer extension [3], [24] which limit the use of public-key cryptography to an ofﬂine reusable pre-processing phase.”
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications (2018, positive): “In brief, we summarize our main contributions as follows: • We introduce Chameleon, a novel mixed SFE framework based on ABY [37] which brings benefits upon efficiency, scalability, and practicality by integrating sequential GCs, fixed-point arithmetic, as well as STP-based protocols for precomputin”
Secure Multiparty Computation for Privacy-Preserving Data Mining (2009, background): “This then allows one to compute an essentially unlimited number of of transfers at the cost of computing hash functions alone (26; 39).”
Efficient Privacy-Preserving Biometric Identification (2011, methodology): “We use OT extension [6] to reduce an arbitrary number of OTs to secure evaluation of just k OTs, where k is a statistical security parameter.”
Single Database Private Information Retrieval with Logarithmic Communication (2004, methodology): “So we abandon the usage of a pseudo-random function, which, however, is a good alternative in practice [ IKNP03 ].”
Improved Garbled Circuit Building Blocks and Applications to Auctions and Computing Minima (2009, methodology): “The extensions of [26] can be used to e ciently reduce the number of computationally expensive public-key operations of OT`”
MP-SPDZ: A Versatile Framework for Multi-Party Computation (2020, neutral): “However, OT extension [45] can be used keep the use of such more expensive schemes at a low constant while using more symmetric cryptography instead.”
Strathprints Institutional Repository (neutral): “It is easy to see that s can be recovered by computing r 1 ⊕ , ..., ⊕ r n and any subset of less than n shares reveals no information about the secret.”
Fairplay - Secure Two-Party Computation System (2004, background): “It would be interesting to explore deployment of further recent enhancements of OT, such as extending a small number of OTs into a large number of OTs using symmetric cryptographic operations alone [24], or using OT variants which are based on the hardness of breaking RSA, rather than the DDH assump”
Efficient Privacy-Preserving Face Recognition (2009, neutral): “We could also use correlationrobust hash functions [19], resulting in slightly more expensive computation of garbled circuits [31] (see below).”
SoK: General Purpose Compilers for Secure Multi-Party Computation (2019, methodology): “Boolean-circuit GMW-based protocols use OTextension [78] to pre-compute ROT correlations, which are then consumed in the online phase of the protocol.”
Cheetah: Lean and Fast Secure Two-Party Deep Neural Network Inference (2022, background): “OT is usually realized by building a few base OT instances with public key cryptography and extending to a large amount of instances with efﬁcient symmetric cryptographic operations (IKNP OT extension [31]).”
MASCOT: Faster Malicious Arithmetic Secure Computation with Oblivious Transfer (2016, methodology): “[23], which we call correlated oblivious product evaluation (COPE).”