Using Homomorphic Encryption for Large Scale Statistical Analysis

By David J. Wu. 2012

56 citations recorded by the source corpus.

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Citations in the corpus

Crypto-Nets: Neural Networks over Encrypted Data (2014, methodology): “Using Homomorphic Encryption (HE) to do machine learning and statistical analysis on encrypted data has been investigated in (Bos et al., 2014; Bost et al., 2014; Graepel et al., 2013; Lauter et al., 2014; Nikolaenko et al., 2013a;b; Wu & Haven, 2012).”
Privacy-preserving Machine Learning as a Service (2018, neutral): “While privacy-preserving machine learning based on secure multi-party computation techniques have been studied in the literature, those approaches focus on traditional machine learning algorithms such as linear regression [6], decision trees [2, 5, 53] and linear classifiers [5, 19].”
Secure Outsourced Matrix Computation and Application to Neural Networks (2018, unknown): “Wu and Haven [48] suggested the first secure inner product method in a SIMD environment.”
Privacy-Preserving Classification on Deep Neural Network (2017, background): “They may be classified according to the learning algorithms which are involved: linear regression training or prediction [35], [47], linear classifiers [7], [8], [22], decision trees [8] or neural networks”
Encrypted statistical machine learning: new privacy preserving methods (2015, methodology): “…classiﬁcation techniques, other privacy preserving statistical methods have been proposed in the literature such as small-P linear regression ( P ≤ 5; Wu and Haven, 2012) and predictive machine learning using pre-trained models (e.g., logistic regression; Bos et al. , 2014) In Section 2 a brief…”
No Peek: A Survey of private distributed deep learning (2018, methodology): “…include de-identiﬁcation methods like anonymization [52], obfuscation methods like diﬀerential privacy [100,101,102] and cryptographic techniques like homomorphic encryption [19,93,96] and secure multi-party computation (MPC) protocols like oblivious transfer [84,47] and garbled circuits [41].”
Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference (2020, background): “We refer the reader to [9], [67] for details.”
Encrypted Accelerated Least Squares Regression (2017, methodology): “Wu and Haven (2012) were the first to tackle linear regression in the context of FHE by using Cramer’s rule for matrix inversion.”