Homomorphic Evaluation of the AES Circuit

By Craig Gentry, Shai Halevi, Nigel P. Smart. 2012

941 citations recorded by the source corpus.

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Citations in the corpus

Pinocchio: Nearly Practical Verifiable Computation (2013, methodology): “In practice however, because they rely on complex Probabilis-tically Checkable Proofs (PCPs) [17] or fully-homomorphic encryption (FHE) [24], the performance is unacceptable – verifying small instances would take hundreds to trillions of years ( § 5.2).”
A Survey on Homomorphic Encryption Schemes (2017, methodology): “Inconclusion, some of the SWHE implementations (leveled-FHE) [Gentry et al. 2012] get closer to a tolerable performance.”
Verifying computations without reexecuting them (2015, methodology): “, in their Muggles work [21], used a complexity-theoretic interactive proof system that applied to computations expressed as certain kinds of circuits; and a couple of years later, Gentry’s breakthrough protocol for fully homomorphic encryption (FHE) [19, 20] led to a line of work (GGP) on non-inter”
Secure Outsourced Matrix Computation and Application to Neural Networks (2018, neutral): “For example, some HE schemes [14, 23] based on the ring learning with errors (RLWE) assumption exploit the structure of Galois group to implement the rotation operation on plaintext slots.”
Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation (2018, methodology): “The SIMD technique enables to parallelize both space and computation time as in BGV scheme [12].”
(Leveled) Fully Homomorphic Encryption without Bootstrapping (2014, positive): “TRADING OFF DEGREE FOR DIMENSION IN GLWE In this section, we present a new technique, termed ring switching in follow-up work [Gentry et al. 2012a], which allows trading off the parameters d and n of the GLWE problem.”
Privacy-Preserving Classification on Deep Neural Network (2017, methodology): “Our solution is based on the BGV encryption scheme [9] with the Smart-Vercauteren ciphertext packing techniques [43] and the Gentry-Halevi-Smart optimizations [18].”
Accelerating Somewhat Homomorphic Evaluation using FPGAs (2015, background): “[25] homomorphic evaluates the AES circuit in about 36 hours resulting in an amortized per block evaluation time of 5 minutes.”
Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence (2020, methodology): “Rather than the original method that requires to estimate an error level before reducing, we adopt Scale method deﬁned in [33].”
Accelerating leveled fully homomorphic encryption using GPU (2014, methodology): “As mentioned in [10], the modulus is an odd number from 512 to 2,048 bits.”
A Fast Single Server Private Information Retrieval Protocol with Low Communication Cost (2014, methodology): “The choice of the BGV parameters is based on the formula given in [37]: φ ( m ) ≥ log( q/σ )( λ +110) 7 .”
Accelerating LTV Based Homomorphic Encryption in Reconfigurable Hardware (2015, background): “[16] manages to evaluate AES roughly an order of magnitude faster than [25].”
cuHE: A Homomorphic Encryption Accelerator Library (2015, methodology): “We further compute, for each level, qi = p0p1 · · · pti where 0 < ti < ti−1 < t as in [19].”
Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network Inference (2019, methodology): “However, we stress that our assumption is no stronger than the same assumption in HE schemes [16, 17, 22, 27] requiring either bootstrapping or relinearization of ciphertexts.”
Improved Security for a Ring-Based Fully Homomorphic Encryption Scheme (2013, background): “This analysis is similar to the ones from [11,8,13] and we refer to [11] for a more complete discussion of assumptions made in deriving parameters.”