Fully Homomorphic Encryption without Modulus Switching from Classical GapSVP

By Zvika Brakerski. 2012

1,387 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Privacy & Security, Algorithms & Complexity

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Citations in the corpus

Advances and Open Problems in Federated Learning (2019, background): “Different flavours of HE exist, ranging from general fully homomorphic encryption (FHE) [176] to the more efficient leveled variants [79, 160, 80, 112], for which several implementations exist [3, 350, 4].”
Gazelle: A Low Latency Framework for Secure Neural Network Inference (2018, methodology): “Examples of depth-bounded homomorphic encryption scheme (called leveled homomorphic encryption or LHE) are the family of lattice-based schemes such as the Brakerski-Gentry-Vaikuntanathan [4] scheme and its derivatives [6], [13].”
A Survey on Homomorphic Encryption Schemes (2017, methodology): “HElib implements the BGV scheme (Brakerski et al. 2011) with Smart-Vercauteren ciphertext packing techniques and some new optimizations. The design and implementation of HElib are documented in Halevi and Shoup (2013a), and algorithms used in HElib are documented in Halevi and Shoup (2014). HElib is”
Simple Encrypted Arithmetic Library - SEAL v2.1 (2016, background): “In practice, some operations in SEAL are done slightly differently, or in slightly more generality , than in textbook-FV (see Section 3.3).”
A Full RNS Variant of FV Like Somewhat Homomorphic Encryption Schemes (2016, methodology): “Further improvements of noise management [7, 6] have been suggested so that, in practice, and given an applicative context, it may be wiser to select an efficient SHE with parameters enabling a sufficient number of operations.”
XONN: XNOR-based Oblivious Deep Neural Network Inference (2019, neutral): “Gazelle uses the Brakerski-Fan-Vercauteren (BFV) scheme [12, 22].”
Towards Deep Neural Network Training on Encrypted Data (2019, background): “Though only a theoretical plausibility result at ﬁrst, the last decade saw major algorithmic improvements (e.g., [6, 5, 13]), resulting in many research prototypes that implement this technology and attempt to use it in different settings (e.g., [4, 12, 17, 8, 14, 19], among others).”
Improved Security for a Ring-Based Fully Homomorphic Encryption Scheme (2013, methodology): “this function for w = 2 is called BitDecomp in [2].”
Reusable garbled circuits and succinct functional encryption (2013, background): “The big leap of the last decade towards computing over encrypted data has been fully homomorphic encryption (FHE) [11–14, 17, 20–22, 38, 51, 52].”
Data Security and Privacy Protection for Cloud Storage: A Survey (2020, methodology): “Inspired by the knowledge of scale, [14] reduce the noise of ciphertext multiplication in LWE-based”
Privacy‐preserving federated learning based on multi‐key homomorphic encryption (2021, background): “In 2019, Chen et al. [20] proposed MK-BFV and MK-CKKS, multi-key variants of the fully homomorphic encryption schemes BFV [21, 22] and CKKS [23].”
Homomorphic Evaluation of the AES Circuit (2012, background): “The other two are the NTRU-like cryptosystem of L ` opez-Alt et al. [15] and the ring-LWE-based ﬁxed-modulus cryptosystem of Brakerski [2].”