Fully Homomorphic Encryption without bootstrapping

By Masahiro Yagisawa. 2015

454 citations recorded by the source corpus.

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Citations in the corpus

A Review of Homomorphic Encryption Libraries for Secure Computation (2018, methodology): “3.1) [22] is based on BFV [4], HElib is based on BGV [1] and TFHE is based on CGGI [2,3].”
XPIR : Private Information Retrieval for Everyone (2016, methodology): “Security reduction is preserved using the same arguments as in [48].”
Compressible FHE with Applications to PIR (2019, methodology): “Some important examples are dimension reduction and modulus reduction [BV14a,BGV12], through which a ciphertext can be transformed to a lower-dimensional vector with coeﬃcients reduced by a smaller modulus, making the ciphertext smaller and also reducing the complexity of its decryption.”
Accelerating Homomorphic Evaluation on Reconfigurable Hardware (2015, methodology): “Examples of SHE schemes are the BGV [8] and LTV [30] schemes and the subsequent YASHE [4] scheme, which are relatively straightforward and conceptually simple as they mainly require polynomial multiplication and (bit level) manipulation of polynomial coefficients for evaluation of ciphertexts (i.e.,”
Accelerating Fully Homomorphic Encryption in Hardware (2015, background): “see [7], [8], [9], [10], we would like to take a snapshot of the attainable hardware performance of the GentryHalevi FHE variant.”
Polynomial multipliers for fully homomorphic encryption on FPGA (2015, methodology): “In Section V, we compare and discuss the proposed solutions and we compare to software runtimes.”
Homomorphic Encryption (2017, methodology): “[19, 16, 14] developed new techniques for better noise control.”
Worst-case to average-case reductions for module lattices (2014, background): “Note that M-LWE has recently been introduced (although not studied) in [7], where it is called Generalized-LWE.”
Bandwidth Efficient PIR from NTRU (2014, methodology): “Here we only need to support a few levels and therefore the full Lopez-Alt, Tromer and Vaikuntanathan scheme is not needed.”
Practical Covertly Secure MPC for Dishonest Majority - Or: Breaking the SPDZ Limits (2013, unknown): “They then each execute the standard BGV key generation procedure, except that this is done with respect to the global element a j .”
Private Predictive Analysis on Encrypted Medical Data (2014, methodology): “Namely, using a different scheme such as the one in [24] could result in significantly faster homomorphic multiplication, at the cost of allowing ciphertexts to grow during homomorphic”
Improved Security for a Ring-Based Fully Homomorphic Encryption Scheme (2013, methodology): “We have proposed a new fully homomorphic encryption scheme based on the scheme by Stehlé and Steinfeld which removes the non-standard decisional small polynomial ratio assumption needed in the homomorphic encryption scheme by López-Alt, Tromer and Vaikuntanathan.”
Security and Privacy Issues in Deep Learning (2018, background): “…various attempts on fully homomorphic encryption (FHE), which allows any computable function to be performed on the encrypted data, have been proposed [Van Dijk et al. 2010; Yagisawa 2015; Brakerski and Vaikuntanathan 2014; Bos et al. 2013; Hesamifard et al. 2017; Ducas and Micciancio 2015].”