Homomorphic Encryption from Learning with Errors: Conceptually-Simpler, Asymptotically-Faster, Attribute-Based

By Craig Gentry, Amit Sahai, Brent Waters. 2013

1,661 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Kim Laine
2. Shai Halevi
3. Wei Dai
4. Hao Chen
5. Craig Gentry
6. Vinod Vaikuntanathan
7. Miran Kim
8. Chris Peikert

In Computational Theory and Mathematics

1. Shai Halevi
2. Kim Laine
3. Hao Chen
4. Vinod Vaikuntanathan
5. Craig Gentry
6. Brent Waters
7. Chris Peikert
8. Amit Sahai

Citations in the corpus

• A Review of Homomorphic Encryption Libraries for Secure Computation (2018, background): “This implies that the noisy ciphertext can be converted into a noise-free ciphertext (of the same plaintext) without the secret key [24,25].”
• A Survey on Homomorphic Encryption Schemes (2017, methodology): “The new scheme is based on [Stehlé and Steinfeld 2011] and it uses a Flattening noise management technique adopted from the flattening technique of [Gentry et al. 2013].”
• Private genome analysis through homomorphic encryption (2015, background): “, [13, 30, 15]) permits encrypted data to be computed on without decryption, so it allows us to evaluate arbitrary arithmetic circuits over encrypted data.”
• A Fast Single Server Private Information Retrieval Protocol with Low Communication Cost (2014, background): “Following the breakthrough, several FHE schemes based on different hardness assumptions have been proposed, e.g. [31, 20, 32].”
• Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning (2016, neutral): “Recently, many fully homomorphic encryption schemes have been proposed such as BGV [31], Bral2 [44] and GSW13 [45].”
• Data Security and Privacy Protection for Cloud Storage: A Survey (2020, methodology): “In order to make multiplication natural for ciphertext, Gentry et al. [38] introduced approximate eigenvector method to make ciphertext be the matrix.”
• 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].”
• Fully Homomorphic Encryption (2014, background): “The lecture is based on the following reference: • Gentry, Sahai, Waters, CRYPTO ’13 [5]”
• Simple Encrypted Arithmetic Library - SEAL v2.1 (2016, methodology): “One such method for the GSW cryptosystem [23] is described in [6].”
• Data Security and Privacy-Preserving in Edge Computing Paradigm: Survey and Open Issues (2018, methodology): “[91] described an improved the Brakerski’s FHE scheme based on the approximate eigen-”
• Candidate Indistinguishability Obfuscation and Functional Encryption for all Circuits (2013, background): “[22], [23], [24], [25], [26]), we show how to obtain an indistinguishability obfuscator for all polynomial-size circuits.”
• Homomorphic Encryption (2017, methodology): “Above we described the basic GSW construction from [48], obtaining a leveled homomorphic encryption with security under the subexponential decision-LWE hardness assumption.”
• Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network Inference (2019, methodology): “It looks similar to the ring variant of GSW [21, 28] but our scheme supports some operations between ciphertexts under different keys.”
• HEPCloud: An FPGA-Based Multicore Processor for FV Somewhat Homomorphic Function Evaluation (2018, background): “schemes to improve the performance of FHE [6], [7], [12], [16], [18], [19], [27], [37].”