Fully Homomorphic Encryption

By Prastudy Fauzi. 2014

296 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning (2020, background): “However, the privacy guarantees of homomorphic encryption depends on the size of the encrypted data (more privacy requires a larger encypted data size), and performing computations in the encrypted domain is computationally expensive [26], [27].”
F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption (2021, background): “Bootstrapping [33] enables FHE computations of unbounded depth.”
Secure Face Matching Using Fully Homomorphic Encryption (2018, methodology): “In this paper, we present a fully homomorphic encryption [13] based approach to (1) cryptographically secure the database of face templates and the feature vector of the presented face image, and (2) perform matching directly in the encrypted domain without the need for decrypting the templates.”
PrivEdge: From Local to Distributed Private Training and Prediction (2020, methodology): “Private training and prediction for MLaaS applications can be addressed by applying cryptography tools such as Fully Homomorphic Encryption (FHE) [31] and Multi-Party Computation (MPC) [32], as well as information theory and differential privacy [33].”
HEPCloud: An FPGA-Based Multicore Processor for FV Somewhat Homomorphic Function Evaluation (2018, background): “For details of the functions, interested readers may follow the original paper [16] or the presentation [38].”
CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning (2019, background): “One is based on Homomorphic Encryption (HE) [24] which allows for compu-”
Security and Privacy Issues in Deep Learning (2018, background): “Homomorphic cryptosystems in early stages were partial homomorphic cryptosystems [41, 42, 43, 44], that showed either additive or multiplicative homomorphism [45].”
Fully homomorphic SIMD operations (2012, methodology): “In a series of work [13–16] Gentry et al. make extensive use of FHE based SIMD operations in a number of contexts related to the BGV cryptosystem [2].”
Towards Fair and Privacy-Preserving Federated Deep Models (2019, background): “Fully homomorphic encryption can support arbitrary computation on ciphertexts, but is less efficient [31].”
Practical Privacy Preserving POI Recommendation (2020, methodology): “The second type is based on cryptography techniques such as homomorphic encryption [22] and secure Multi-Party Computation (MPC) [61], and typical methods include [2, 6, 19, 41, 43].”
Function Secret Sharing (2015, methodology): “We show that FSS schemes enjoy a convenient bootstrapping property, when paired with fully homomorphic encryption (FHE).”