Privacy Preserving Classification of EEG Data Using Machine Learning and Homomorphic Encryption

Privacy Preserving Classification of EEG Data Using Machine Learning and Homomorphic Encryption

Data privacy is a major concern when accessing and processing sensitive medical data. A promising approach among privacy-preserving techniques is homomorphic encryption (HE), which allows for computations to be performed on encrypted data. Currently, HE still faces practical limitations related to h...

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Bibliographic Details
Main Authors: Andreea Bianca Popescu, Ioana Antonia Taca, Cosmin Ioan Nita, Anamaria Vizitiu, Robert Demeter, Constantin Suciu, Lucian Mihai Itu
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Applied Sciences
Subjects:
privacy-preserving computations
homomorphic encryption
machine learning
EEG signals
Online Access:https://www.mdpi.com/2076-3417/11/16/7360

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