A fully homomorphic encryption scheme

By Craig Gentry. 2009

3,084 citations recorded by the source corpus.

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In Privacy & Security

Citations in the corpus

Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning (2020, background): “size), and performing computations in the encrypted domain is computationally expensive [25], [26].”
Wireless Network Intelligence at the Edge (2018, background): “Extra coding, such as homomorphic encryption [123], may guarantee privacy, but its processing delay may exceed the lowlatency deadline.”
Pinocchio: Nearly Practical Verifiable Computation (2013, methodology): “In practice however, because they rely on complex Probabilistically 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.”
CryptoDL: Deep Neural Networks over Encrypted Data (2017, methodology): “In bootstrapping technique, the amount of noise is decreased without needing to access sk [11].”
Gazelle: A Low Latency Framework for Secure Neural Network Inference (2018, methodology): “Homomorphic Encryption: Fully Homomorphic Encryption, or FHE, is an encryption method that allows anyone to compute an arbitrary function f on an encryption of x , without decrypting it and without knowledge of the private key [5], [14], [31].”
A Survey on Homomorphic Encryption Schemes (2017, background): “Gentry’s ﬁrst FHE scheme in his PhD thesis [Gentry 2009] is a GGH-type of encryption scheme, where GGH is proposed originally by Goldreich et al. [Goldreich et al. 1997].”
Privacy-preserving Machine Learning as a Service (2018, neutral): “The original fully homomorphic encryption (FHE) scheme presented in Gentry’s seminal paper [14] was highly inefficient but since then, significantly more practical schemes have been developed.”
Polynomial multipliers for fully homomorphic encryption on FPGA (2015, background): “It has been an open question till the seminal work of Gentry [2] who was the first to propose such scheme.”
Secure Face Matching Using Fully Homomorphic Encryption (2018, methodology): “Our approach is based on fully homomorphic encryption (FHE) schemes [16] that can support a series of basic arithmetic operations such as additions and multiplications over encrypted data.”
Faster Fully Homomorphic Encryption (2010, background): “Our definitions are based on [9,8], but we slightly relax the definition of decryption correctness, to allow a negligible probability of error.”
Leakproofing the Singularity Artificial Intelligence Confinement Problem (2012, background): “The source code and hardware configuration of the system needs to be obfuscated (Yampolskiy & Govindaraju, 2007a) and important modules of the program should be provided only in the homomorphicly encrypted (Gentry, 2009) form, meaning that it could be used for computation or self-improvement (Hall, ”
Accelerating fully homomorphic encryption using GPU (2012, neutral): “As mentioned before, the ﬁrst FHE is proposed by Gentry in [1], [8].”
This Paper Is Included in the Proceedings of the 12th Usenix Symposium on Operating Systems Design and Implementation (osdi '16). Ryoan: a Distributed Sandbox for Untrusted Computation on Secret Data Ryoan: a Distributed Sandbox for Untrusted Computation on Secret Data (neutral): “Homomorphic encryption [25, 38] and order-preserving encryption [22] share similar motivations with Ryoan.”
Accelerating Fully Homomorphic Encryption over the Integers with Super-size Hardware Multiplier and Modular Reduction (2013, methodology): “To date, many FHE schemes based on different computationally hard problems have been proposed [1 – 9], as well as their software implementations [8 – 13].”