Foundations of garbled circuits

By Mihir Bellare, Viet Tung Hoang, Phillip Rogaway. Conference on Computer and Communications Security, 2012

620 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/646913a2c744da069dd85ccbbed34588362f7537

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Citations in the corpus

Fast Garbling of Circuits Under Standard Assumptions (2015, neutral): “[3] in which a garbling scheme consists of 4 algorithms: – Garble(1n, c) → (C, e, d) is an algorithm that takes as input a security parameter 1n and a description of a boolean circuit c and returns a triple (C, e, d), where C represents a garbled circuit, e represents input encoding information (i.”
TinyGarble: Highly Compressed and Scalable Sequential Garbled Circuits (2015, methodology): “The research on producing Boolean functions for SFE can be roughly classified into two categories: optimizations of cryptographic constructs and protocols such as [2], [3], [42], [44], [60], [72], and compiler/engineering techniques including but not limited to [23], [32], [36], [46], [47], [53],…”
Faster Secure Two-Party Computation in the Single-Execution Setting (2017, background): “[6]), let H be collision-resistant, and assume the decisional Diffie-Hellman assumption holds.”
A Pragmatic Introduction to Secure Multi-Party Computation (2019, background): “Their design is based on a dual-key cipher (Bellare et al., 2012), where two keys are both needed to decrypt a ciphertext.”
Garbling Gadgets for Boolean and Arithmetic Circuits (2016, methodology): “We use the garbling schemes abstraction and security deﬁnitions of Bellare, Hoang and Rogaway [7].”
DeepSecure: Scalable Provably-Secure Deep Learning (2017, background): “GC is proven to be secure in HbC adversary model [18]; thereby, any input from either client or server(s) to GC will be kept private during the protocol execution.”
SecureML: A System for Scalable Privacy-Preserving Machine Learning (2017, methodology): “We require the garbling scheme to satisfy the standard security properties formalized in [13].”
Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges (2022, methodology): “It provides multiple cryptographic primitives which allows for secure computations such as Secret Sharing [135], Zero Knowledge Proofs [136] and Garbled Circuits [137].”
Efficient Garbling from a Fixed-Key Blockcipher (2013, methodology): “We adopt the definitions of BHR [5] lifted to the randompermutation model (RPM).”
SoK: General Purpose Compilers for Secure Multi-Party Computation (2019, background): “Garbled circuits are so useful and ubiquitous in cryptography that it has been argued that circuit garbling should be considered as a fundamental cryptographic primitive (like encryption or signatures) rather than as a protocol for two-party secure computation [13].”
Two Halves Make a Whole - Reducing Data Transfer in Garbled Circuits Using Half Gates (2015, methodology): “1 The formalization of [5] allows for garbling of any form of computation.”
Attribute-Based Encryption for Circuits from Multilinear Maps (2012, methodology): “We now deﬁne our notation for circuits that adapts the model and notation of Bellare, Hoang, and Rogaway [BHR12] (Section 2.3).”
Fast and Secure Three-party Computation: The Garbled Circuit Approach (2015, methodology): “We employ the abstraction of garbling schemes [BHR12b] introduced by Bellare et al. Below is a summary of garbling scheme syntax and security: A garbling scheme is a four-tuple of algorithms G = ( Gb , En , De , Ev ) where: Gb is a randomized garbling algorithm that transforms function f into a…”