Full Citation
Title: A review of privacy-preserving techniques for deep learning
Citation Type: Journal Article
Publication Year: 2020
ISBN:
ISSN: 09252312
DOI: 10.1016/j.neucom.2019.11.041
NSFID:
PMCID:
PMID:
Abstract: Deep learning is one of the advanced approaches of machine learning, and has attracted a growing attention in the recent years. It is used nowadays in different domains and applications such as pattern recognition, medical prediction, and speech recognition. Differently from traditional learning algorithms, deep learning can overcome the dependency on hand-designed features. Deep learning experience is particularly improved by leveraging powerful infrastructures such as clouds and adopting collaborative learning for model training. However, this comes at the expense of privacy, especially when sensitive data are processed during the training and the prediction phases, as well as when training model is shared. In this paper, we provide a review of the existing privacy-preserving deep learning techniques, and propose a novel multi-level taxonomy, which categorizes the current state-of-the-art privacy-preserving deep learning techniques on the basis of privacy-preserving tasks at the top level, and key technological concepts at the base level. This survey further summarizes evaluation results of the reviewed solutions with respect to defined performance metrics. In addition, it derives a set of learned lessons from each privacypreserving task. Finally, it highlights open research challenges and provides some recommendations as future research directions
Url: https://linkinghub.elsevier.com/retrieve/pii/S0925231219316431
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Authors: Boulemtafes, Amine; Derhab, Abdelouahid; Challal, Yacine
Periodical (Full): Neurocomputing
Issue: 7
Volume: 384
Pages: 21-45
Data Collections: IPUMS USA
Topics: Health, Other
Countries: