How to generate and exchange secrets

By A. Yao. 1986

Subfields: 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 Privacy & Security

1. Adrià Gascón
2. Dan Boneh
3. Peter Kairouz
4. Badih Ghazi
5. David E. Evans
6. Rasmus Pagh
7. Borja Balle
8. Shai Halevi

Citations in the corpus

• Advances and Open Problems in Federated Learning (2019, background): “of having a set of parties compute an agreed-upon function of their private inputs in a way that only reveals the intended output to each of the parties. This area was kicked off in the 1980’s by Yao [422]. Thanks to both theoretical and engineering breakthroughs, the field has moved from being of a ”
• Real-World Image Datasets for Federated Learning (2019, neutral): “…the challenge of data privacy and security in 88 127 Figure 2: The distribution of labels per image. the machine learning process, many privacy-preserving machine learning methods have been proposed in the past, such as secure multi-party computing (MPC) in the 1980s [ Yao, 1982; Yao, 1986 ] .”
• Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, methodology): “Several generic techniques have been developed for MPC including Yao’s garbled circuits protocol [70], the GMW protocol [31], and other secret-sharing based techniques [7, 41, 50].”
• I-Pic: A Platform for Privacy-Compliant Image Capture (2016, methodology): “The Capture Agent and the Bystander Agent compare extracted features and a bystander’s classifier weight vector by implementing a secure dot-product protocol [8] followed by a secure threshold comparison protocol based on garbled circuits [9].”
• DeepSecure: Scalable Provably-Secure Deep Learning (2017, background): “ceive xr among the sender’s messages without the sender learning the index r, while the receiver only obtains one of the n possible messages [11]. 2.2.2 Garbled Circuit Yao’s garbled circuit protocol [12] is a cryptographic protocol in which two parties, Alice and Bob, jointly compute a function f (”
• Gazelle: A Low Latency Framework for Secure Neural Network Inference (2018, methodology): “For the 2PC framework, we use Yao’s Garbled circuits [40].”
• Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications (2018, methodology): “, 76, 82], secure auctions [42], privacy-preserving machine learning [38], and data mining [63, 64, 74]. In 1986, Yao introduced a generic protocol for SFE, called Yao’s Garbled Circuit (GC) protocol [89]. The Goldreich-Micali-Wigderson (GMW) protocol [45] is another SFE protocol that was introduced”
• Blind Justice: Fairness with Encrypted Sensitive Attributes (2018, methodology): “Several generic protocols for MPC exists, such as the ones based on arithmetic sharing (Damg˚ard et al., 2012), garbled circuits (Yao, 1986), or GMW (Goldreich et al., 1987), with several available implementations (Demmler et al., 2015b; Zahur & Evans, 2015b).”
• Cryptϵ: Crypto-Assisted Differential Privacy on Untrusted Servers (2019, methodology): “of a pseudo-random function. In addition to the operations supported by an LHE scheme, labHE supports multiplication of two labHE ciphers (details in Appendix A ). Secure Computation: Garbled circuit [73, 103] is a generic method for secure computation. Two data owners with respective private inputs”
• Two Round Multiparty Computation via Multi-key FHE (2016, background): “This problem was initially studied by Yao [Yao82, Yao86], in the case of two honest-but-curious parties (who follow the protocol honestly but hope to learn information from its execution) and later by Goldreich, Micali and Wigderson [GMW87] in the case of an arbitrary number of fully malicious…”
• Universally composable two-party and multi-party secure computation (2002, background): “An alternative general construction of twoparty protocols, attributed to Yao [47], has the advantage that only a small constant number of rounds is required in order to compute each output value, regardless of the complexity of the evaluated functionality.”
• Large-Scale Secure Computation: Multi-party Computation for (Parallel) RAM Programs (2015, methodology): “In contrast, methods such as secure multi-party computation (MPC), introduced in the seminal works of Yao [Yao86] and Goldreich, Micali and Wigderson [GMW87], enable securely and privately performing any computation on individuals private inputs (assuming some fraction of the parties are honest).”
• Foundations of Cryptography: Volume 1, Basic Tools (2001, unknown): “[211] for the two-party case and [113] for the case of more than two parties).”