How to play ANY mental game

By Oded Goldreich, Silvio Micali, Avi Wigderson. 1987

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In Game & Decision Theory

Citations in the corpus

Advances and Open Problems in Federated Learning (2019, background): “It has been known for several decades that any function can be securely computed, even in the presence of malicious adversaries [183].”
Secure Federated Submodel Learning (2019, background): “These basic settings make the secure aggregation protocol different from other relevant work about oblivious addition [19]–[21], or, more generally, secure multiparty computation [63]–[66], which requires direct peer-to-peer communication between clients; assumes the availability of multiple noncoll”
Federated Machine Learning (2019, methodology): “SMC provides formal privacy proof for these protocols [25].”
Distributed learning of deep neural network over multiple agents (2018, background): “Such algorithms form a subset inside the broader realm of multi-party protocol problems involving secure computation over several parties [26, 27].”
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].”
Prio: Private, Robust, and Scalable Computation of Aggregate Statistics (2017, background): “In theory, secure multi-party computation (MPC) protocols [11, 15, 68, 90, 123] allow a set of servers, with some”
A Comprehensive Comparison of Multiparty Secure Additions with Differential Privacy (2015, background): “protocol has been extended to securemultiparty computations in [31] and to scenarioswith active adversaries [38].”
Privacy Preserving Data Mining (2015, methodology): “It was extended to multiparty computation by Goldreich [12] Yao’s Millionaire Problem Essentially the problem is Alice and Bob are two millionaires who want to find out who is richer without revealing the precise amount of their wealth.”
On the Difficulties of Disclosure Prevention in Statistical Databases or The Case for Differential Privacy (2010, background): “, xn), where each xi is the input to f provided by the ith player; the privacy guarantee is that no information is leaked about the individual values other than that which can be deduced from the output [21] (see [20] for a comprehensive treatment).”
Distributed Private Data Analysis: On Simultaneously Solving How and What (2008, background): “SFE has been the subject of extensive cryptographic research (initiated in [26, 18, 4, 1]), and SFE protocols exist for any feasible function f(·) in a variety of general settings.”
Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification (2004, background): “The history of the SMC problem is extensive since it was introduced by Yao [26] and extended by Goldreich, Micali, and Wigderson [16], and by many others.”
Gazelle: A Low Latency Framework for Secure Neural Network Inference (2018, methodology): “Two Party Computation: Yao’s garbled circuits [40] and the Goldreich-Micali-Wigderson (GMW) protocol [17] are two leading methods for the task of two-party secure computation (2PC).”
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications (2018, methodology): “The Goldreich-Micali-Wigderson (GMW) protocol [42] is another SFE protocol that was introduced in 1987.”
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).”