Fully Homomorphic Encryption over the Integers

By Marten van Dijk, Craig Gentry, Shai Halevi, Vinod Vaikuntanathan. 2010

1,938 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

On the Capacity of Secure Distributed Matrix Multiplication (2018, background): “Naturally, it is often used as a solution to secure function computation problems and other types of security problems [4], [5].”
A Comprehensive Comparison of Multiparty Secure Additions with Differential Privacy (2015, background): “The price for such flexibility in choosing f is high complexity of computations [28], [53].”
Compressible FHE with Applications to PIR (2019, methodology): “Some prior works included a “post-evaluation” ciphertext compression techniques, such as the work of van Dijk et al. [vDGHV10] for integer-based HE, and the work of Hohenberger et al. for attribute-based encryption [GHW11].”
Faster Fully Homomorphic Encryption (2010, background): “Note that this condition is less restrictive than the corresponding one used in [11, 27, 7] (i.”
A Comprehensive Survey of Privacy-preserving Federated Learning (2021, neutral): “According to the supported operators, homomorphic encryption methods can be divided into two categories: partially homomorphic encryption [134] and fully homomorphic encryption [176].”
Modular Hardware Architecture for Somewhat Homomorphic Function Evaluation (2015, background): “Since then, many researchers have followed the blueprints set out by Gentry’s proposal with an objective to improve the performance of FHE [6,7,14,17,20,24,25,32].”
Bootstrapping for Approximate Homomorphic Encryption (2018, background): “There have been a number of studies [22, 8, 9, 6, 7, 25, 30, 11, 5, 31, 19, 16] to improve the eﬃciency of HE cryptosystem after Gentry’s blueprint [26].”
Privacy-Preserving ECG Classification With Branching Programs and Neural Networks (2011, background): “homomorphic cryptosystems [38], [39], [49] could open new re-”
Non-interactive Verifiable Computing: Outsourcing Computation to Untrusted Workers (2010, methodology): “∗This research was supported in part by the US Army Research laboratory and the UK Ministry of Defence under Agreement Number W911NF-06-3-0001, as well as by the National Science Foundatio (NSF), under award number CCF-0424422.”
(Leveled) Fully Homomorphic Encryption without Bootstrapping (2014, positive): “Until recently, all FHE schemes [Brakerski and Vaikuntanathan 2011a; Coron et al. 2011; Gentry 2009b; Gentry and Halevi 2011b; Smart and Vercauteren 2010; van Dijk et al. 2010] followed the same blueprint, namely, the one laid out in Gentry’s original construction [Gentry 2009a, 2009b].”
Data Security and Privacy-Preserving in Edge Computing Paradigm: Survey and Open Issues (2018, methodology): “[89] introduced a simple FHE method based on the approximate greatest common divisor (GCD) problem, the boots-trappable encryption process was using only elementary modular arithmetic over the integers instead of ideal lattices in Gentry’09.”
Token-Based Cloud Computing Secure Outsourcing of Data and Arbitrary Computations with Lower Latency (2010, background): “Recently, there are several proposals starting with [12] and subsequent extensions and improvements of [9,26].”