Protocols for secure computations

By A. Yao. 1982

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In Privacy & Security

Citations in the corpus

Advances and Open Problems in Federated Learning (2019, methodology): “Cryptographic methods for computing on encrypted data were developed starting in the early 1980s [2], [3], and Agrawal and Srikant [4] and Vaidya et al.”
Backdoor attacks and defenses in feature-partitioned collaborative learning (2020, background): “Privacy-preserving techniques such as Homomorphic Encryption (HE) (Rivest et al., 1978; Acar et al., 2018), Secure Multi-party Computation (SMPC) (Yao, 1982) and Differential Privacy (DP) (Dwork, 2006) are typically applied in these frameworks to preserve user privacy and data confidentiality.”
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning (2020, methodology): “MPC-based techniques mainly utilize Yao’s garbled circuits or secret sharing (e.g., [14], [15], [16], [17]).”
A Communication Efficient Vertical Federated Learning Framework (2019, background): “Existing architectures for VFL still face several critical challenges and communication overhead is a major bottleneck since privacy-preserving computations, such as Homomorphic Encryption (HE) [4, 5] and Multi-party Computation (SMPC) [6], are typically applied to transmitted data, and per-iteratio”
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 ] .”
Federated Machine Learning (2019, methodology): “sed secure protocols for multi-party linear regression and classification. Ref [68] proposed secure multi-party gradient descent methods. The above works all used secure multi-party computation (SMC) [25,72] for privacy guarantees. Nikolaenko et al.[48] implemented a privacy-preserving protocol for ”
A Review of Homomorphic Encryption Libraries for Secure Computation (2018, neutral): “ Media Lab The authors would like to thank Niyaatii Swami for proof reading the paper 2 A Review of Homomorphic Encryption Libraries for Secure Computation Somewhat Homomorphic Encryption schemes are [4,5,6,7,8]. PartiallyHomomorphic Encryption schemes such as [9,10,11,12,13,14,15,16,17,18,19] allow”
On the Capacity of Secure Distributed Matrix Multiplication (2018, background): “Some previous works include secure two-party computation [1] which proposed using one-way functions to achieve security, and secure multi-party computation [2], [3] to name a few.”
Privacy Preserving Data Mining (2015, methodology): “In the new algorithm, features removable without affecting classification accuracy are suppressed.”
Learning privately from multiparty data (2016, background): “However, averaging is not applicable to classifiers with non-numerical parameters such as decision trees, nor to a collection of different classifier types.”
Differentially Private Empirical Risk Minimization (2009, neutral): “An alternative line of privacy work is in the Secure Multiparty Computation setting due to Yao (1982), where the sensitive data is split across multiple hostile databases, and the goal is to compute a function on the union of these databases.”
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.”
Efficient Private Statistics with Succinct Sketches (2015, background): “Mood et al. [54] propose a privacy-preserving participatory sensing application which allows users to locate nearby friends without disclosing exact locations, via secure function evaluation [65], but do not address the problem of scaling to large streams/number of users.”
A Communication Efficient Collaborative Learning Framework for Distributed Features (2019, methodology): “hm parameters needs contributions from all parties. In addition, to prevent data leakage, privacypreserving techniques, such as Homomorphic Encryption (HE) [11], Secure Multi-party Computation (SMPC) [12] are typically applied to transmitted data [4, 10, 9], adding expensive communication overhead t”
Privacy-preserving matrix factorization (2013, methodology): “Our approach is based on Yao’s garbled circuits.”
Zero-Knowledge Contingent Payments Revisited: Attacks and Payments for Services (2017, methodology): “Because both buyer and seller want to keep k and s secret respectively, they will have to use a secure two-party computation protocol, such as Yao’s garbled circuit [51] to compute F ′.”