Homomorphic Encryption for Arithmetic of Approximate Numbers

By Jung Hee Cheon, Andrey Kim, Miran Kim, Yongsoo Song. 2017

2,700 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

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Citations in the corpus

HEAX: An Architecture for Computing on Encrypted Data (2019, methodology): “2 FHE Parameters and Security Guarantees The security guarantees of HEAX directly derives from the CKKS scheme [17] since the functionality of the scheme is not altered.”
A Survey on Homomorphic Encryption Schemes (2017, methodology): “The presented scheme in [Cheon et al. 2016] can handle efﬁciently large message spaces.”
Low Latency Privacy Preserving Inference (2018, methodology): “The recently introduced HEAAN scheme (Cheon et al., 2017) is more tolerant towards message growth but even HEAAN would not be able to operate efficiently on deep networks.”
Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network Inference (2019, methodology): “The CKKS scheme [16] is a leveled HE scheme with support for approximate fixed-point arithmetic.”
Scaling Distributed Machine Learning with In-Network Aggregation (2019, positive): “For instance, recent work by Cheon et al. [14] developed a homomorphic encryption scheme for approximate arithmetic.”
Secure Outsourced Matrix Computation and Application to Neural Networks (2018, neutral): “In the current literature, most of practical HE schemes [8, 9, 14, 19] support their own packing methods to achieve better amortized complexity of homomorphic operations.”
Bootstrapping for Approximate Homomorphic Encryption (2018, methodology): “We will make the use of the following lemmas from [14] for noise estimation.”
Simple Encrypted Arithmetic Library v2.3.0 (2017, methodology): “The first such encryption scheme was invented by Craig Gentry in 2009 [22], and since then researchers have introduced a number of new and more efficient fully homomorphic encryption schemes [11, 10, 7, 9, 21, 29, 5, 24, 15].”
Data Security and Privacy Protection for Cloud Storage: A Survey (2020, methodology): “[20] proposed a RLWE full encryption scheme to support floating-point cal-”
Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation (2018, methodology): “For the evaluation of the least squares polynomial g(x) at (–zi β), we adapt the polynomial evaluation algorithm, denoted by PolyEval(⋅), suggested in [18].”
F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption (2021, background): “BGV [14], B/FV [13, 28], GSW [35], and CKKS [17] are popular FHE schemes.”
HARK Side of Deep Learning - From Grad Student Descent to Automated Machine Learning (2019, methodology): “With the use of homomorphic encryption , deep learning model inference on encrypted data was shown to be possible with a little trade-off from accuracy as well [94, 95].”
CHET: Compiler and Runtime for Homomorphic Evaluation of Tensor Programs (2018, methodology): “Modern FHE schemes [10, 20, 16] organically support integer and fixed-precision arithmetic dramatically reducing the size of the circuits required.”
OpenFHE: Open-Source Fully Homomorphic Encryption Library (2022, positive): “The third, and most recent, class supports approximate computations over vectors of real and complex numbers, and is represented by the Cheon-Kim-Kim-Song (CKKS) scheme [26].”
Logistic regression model training based on the approximate homomorphic encryption (2018, methodology): “Refer [14] for the technical details and noise analysis.”