Name
Abstract | ||
|---|---|---|
Privacy-preserving deep learning with homomorphic encryption (HE) is a novel and promising research area aimed at designing deep learning solutions that operate while guaranteeing the privacy of user data. Designing privacy-preserving deep learning solutions requires one to completely rethink and redesign deep learning models and algorithms to match the severe technological and algorithmic constraints of HE. This paper provides an introduction to this complex research area as well as a methodology for designing privacy-preserving convolutional neural networks (CNNs). This methodology was applied to the design of a privacy-preserving version of the well-known LeNet-1 CNN, which was successfully operated on two benchmark datasets for image classification. Furthermore, this paper details and comments on the research challenges and software resources available for privacy-preserving deep learning with HE. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/MCI.2022.3180883 | IEEE Computational Intelligence Magazine |
Keywords | DocType | Volume |
image classification,CNN,user data privacy,HE,designing privacy preserving deep learning solutions,privacy preserving convolutional neural networks,privacy preserving version,deep learning models,homomorphic encryption | Journal | 17 |
Issue | ISSN | Citations |
3 | 1556-603X | 0 |
PageRank | References | Authors |
0.34 | 13 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alessandro Falcetta | 1 | 0 | 0.34 |
| Manuel Roveri | 2 | 272 | 30.19 |