Fully homomorphic encryption using ideal lattices

By Craig Gentry. 2009

7,168 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Privacy & Security, Mathematical Optimization

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Citations in the corpus

Advances and Open Problems in Federated Learning (2019, background): “Different flavours of HE exist, ranging from general fully homomorphic encryption (FHE) [176] to the more efficient leveled variants [79, 160, 80, 112], for which several implementations exist [3, 350, 4].”
Secure Single-Server Aggregation with (Poly)Logarithmic Overhead (2020, background): “Note that there exist relevant solutions based on homomorphic encryption (HE) [17, 18, 32], where the server computes the sum of encrypted inputs under a key shared among the clients.”
Privacy in Deep Learning: A Survey (2020, neutral): “HE [101] allows computation over encrypted data.”
HEAX: An Architecture for Computing on Encrypted Data (2019, background): “In 2009, a new class of cryptosystems, called Fully Homo-morphic Encryption (FHE) [33], was introduced that allows arbitrary computation on encrypted data .”
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption (2017, background): “Paillier [Paillier, 1999] or Brakerski-Gentry-Vaikuntanathan cryptosystems [Gentry, 2009], secure multi-party computation, e.”
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].”
Privacy-Preserving Ridge Regression on Hundreds of Millions of Records (2013, background): “The second is based on homomorphic encryption where the learning algorithm is executed over encrypted data [2] and a trusted third party is trusted to only decrypt the final encrypted model.”
Crypto-Nets: Neural Networks over Encrypted Data (2014, background): “A fully homomorphic encryption scheme (Gentry, 2009) allows arbitrary operations over encrypted data and therefore, any function can be computed.”
Machine Unlearning (2019, background): “Homomorphic Encryption: Homomorphic encryption is a set of schemes that enable computation on encrypted data without having to decrypt it [32].”
VC3: Trustworthy Data Analytics in the Cloud Using SGX (2015, methodology): “For instance, data confidentiality can be achieved using fully homomorphic encryption (FHE), which enables cloud processing to be carried out on encrypted *Work done while at Microsoft Research; affiliated with Horst Görtz Institut (HGI) at Ruhr-Universität Bochum, Germany. data [22].”
Opaque: An Oblivious and Encrypted Distributed Analytics Platform (2017, background): “Fully homomorphic encryption [11, 12] permits computing any function on encrypted data, but is prohibitively slow.”
Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference (2020, methodology): “A. Homomorphic Encryption: The Basics HE is a privacy-preserving encryption technique that enables computation over encrypted data, which was ﬁrst shown to be possible by Gentry [24].”
Challenges in Deploying Machine Learning: A Survey of Case Studies (2020, background): “Some of the ways this trade-of is resolved in practice are diferential privacy that explicitly corrupts the data [42], homomorphic encryption that restricts the class of learning algorithm but allows for training on encrypted data [50], and federated learning that distributes training across persona”
Accelerating Somewhat Homomorphic Evaluation using FPGAs (2015, background): “The introduction of the first plausible FHE construction by Gentry in 2009 [19, 20], fueled the race to develop more efficient schemes.”
Succinct Malleable NIZKs and an Application to Compact Shuffles (2013, background): “Recently, malleability is increasingly being viewed more as a feature than as a bug [27,28,18,1,13,16,6].”