A Survey of Homomorphic Encryption for Nonspecialists
By Caroline Fontaine, Fabien Galand. EURASIP Journal on Information Security, 2007
532 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/a6ffdbc680b0cf89fcf96cebf67cda3570f424b5
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In Artificial Intelligence
- Hamed Haddadi
- Yoichi Sato
- Ryo Yonetani
- Ali Shahin Shamsabadi
- Yonina C. Eldar
- Kris M. Kitani
- Kobi Cohen
- Benjamin S. Glicksberg
In Computer Vision and Pattern Recognition
- Yoichi Sato
- Ryo Yonetani
- Kris M. Kitani
- Fei Wang
- Ali Shahin Shamsabadi
- Kobi Cohen
- Benjamin S. Glicksberg
- Chang Su
In Electrical and Electronic Engineering
- Yonina C. Eldar
- Kobi Cohen
- Vishnu Naresh Boddeti
- Mauro Conti
- H. Vincent Poor
- Fei Wang
- Nir Shlezinger
- Hamed Haddadi
Citations in the corpus
- A privacy threat analysis framework: supporting the elicitation and fulfillment of privacy requirements (2011, methodology): “Encryption techniques Symmetric key & public key encryption [75] × Deniable encryption × × Homomorphic encryption [76] × Verifiable encryption [77] ×”
- Federated Learning for Healthcare Informatics (2019, background): “Homomorphic encryption is a public key system, where any party can encrypt its data with a known public key and perform calculations with data encrypted by others with the same public key [23].”
- Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption (2017, background): “An attractive solution for privacy-preserving learning without such trade-off is the use of homomorphic cryptosystems — a class of cryptosystems that can allow for basic arithmetic operations over encrypted data [22].”
- Federated Learning: A signal processing perspective (2021, background): “, averaging-based aggregation, on the encrypted data without having to first decrypt it [31].”
- Privacy-Preserving Machine Learning for Speech Processing (2012, methodology): “Please refer to [Fontaine and Galand, 2007a] for a survey.”
- A Survey on Homomorphic Encryption Schemes (2017, methodology): “After [Fontaine and Galand 2007] and [Akinwande 2009], many HE schemes were introduced.”
- Deep Private-Feature Extraction (2018, background): “Current solutions to this problem, such as cryptography [5], [6], or complete data isolation and local processing [7], are not efficient for big data and techniques relying on deep learning [8], [9].”