Somewhat Practical Fully Homomorphic Encryption

By Junfeng Fan, Frederik Vercauteren. 2012

2,257 citations recorded by the source corpus.

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In Privacy & Security

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].”
HEAX: An Architecture for Computing on Encrypted Data (2019, background): “st critical obstacle today to deploy FHE at large-scale is the enormous computation overhead compared to a plaintext counterpart in which data is not kept confidential. MostFHEschemes,i.e.,BGV[11],BFV[31],andTFHE[18] schemes, perform exact computation on encrypted data. A recently proposed FHE schem”
A Review of Homomorphic Encryption Libraries for Secure Computation (2018, methodology): “Some examples of Somewhat Homomorphic Encryption schemes are [4,5,6,7,8].”
PIR with Compressed Queries and Amortized Query Processing (2018, methodology): “, BV [22], BGV [20], FV [40]) passing in values of i ∈ [0, n− 1] to obtain the n ciphertexts.”
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 High-Speed Accelerator for Homomorphic Encryption using the Karatsuba Algorithm (2017, methodology): “This paper focuses on the encryption operation for the Ring-Learning With Error (R-LWE) [17] based schemes, and in particular the Fan Vercauteren (FV) [7] scheme.”
Improved Security for a Ring-Based Fully Homomorphic Encryption Scheme (2013, methodology): “The deduction of noise bounds mostly follows the basic multiplication section of [8], since ciphertexts and the decryption algorithm in LHE have a very similar structure to those in [8].”
Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network Inference (2019, methodology): “In this section, we present multi-key variants of the BFV [6, 22] and CKKS [16] schemes.”
Implementation and Performance Evaluation of RNS Variants of the BFV Homomorphic Encryption Scheme (2019, methodology): “One of the most promising schemes is the Fan-Vercauteren variant of Brakerski’s scale-invariant scheme [3], [5], which we refer to as BFV in this paper.”
Simple Encrypted Arithmetic Library v2.3.0 (2017, methodology): “In [21] this assumption is referred to as a form of weak circular security.”
An Improved RNS Variant of the BFV Homomorphic Encryption Scheme (2019, methodology): “7 Fan and Vercauteren described in [8] a second relinearization procedure, using a technique of Gentry et al.”
Communication-Computation Trade-offs in PIR (2019, methodology): “Below, we recall the Fan–Vercauteren (FV) homomorphic encryption scheme [28].”
Encrypted statistical machine learning: new privacy preserving methods (2015, methodology): “…key must be provided so that the algorithm can produce encrypted versions of these, > fit - CRF.fit(forest, X, y, resamp=8, pk=keys$pk) For the Fan and Vercauteren (2012) scheme in particular, it is possible to add un-encrypted values to encrypted ones knowing only the parameters of the…”
On Deploying Secure Computing Commercially: Private Intersection-Sum Protocols and their Business Applications (2019, methodology): “The most efficient schemes of this type are based on the hardness of Ring-Learning-With-Errors [9, 8, 27, 23].”
Towards practical privacy-preserving genome-wide association study (2018, methodology): “Given the parameters d , q , and t , and distributions χ key , and χ err , and using bold notation for a vector of two polynomials, we deﬁne the encryption, and decryption mechanism of the homomorphic encrytion scheme introduced by Fan and Vercauteren: – Encrypt( pk , m ): By multiplying the message”
Secure Outsourced Matrix Computation and Application to Neural Networks (2018, neutral): “However, most of HE schemes [9, 14, 19] have a quite large number of plaintext slots (e.”
ML Confidential: Machine Learning on Encrypted Data (2012, methodology): “We also use the optimization proposed in [6] to choose the secret key sk = s randomly with binary coefficients in {0, 1}.”