Analysis of Privacy-Enhancing Technologies in Open-Source Federated Learning Frameworks for Driver Activity Recognition

Analysis of Privacy-Enhancing Technologies in Open-Source Federated Learning Frameworks for Driver Activity Recognition

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Wearable devices and smartphones that are used to monitor the activity and the state of the driver collect a lot of sensitive data such as audio, video, location and even health data. The analysis and processing of such data require observing the strict legal requirements for personal data security...

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Bibliographic Details
Main Authors: Filippov, E. (Author), Fomichov, D. (Author), Kholod, I. (Author), Novikova, E. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
Activity monitoring
Differential privacies
differential privacy
driver activity monitoring
Driver activity monitoring
federated learning
Federated learning
homomorphic encryption
Ho-momorphic encryptions
Homomorphic-encryptions
Learning frameworks
Open-source
Open-source federated learning framework
open-source federated learning frameworks
privacy
Privacy
Privacy-preserving techniques
Secure multi-party computation
secure multi-party computations
Smartphones
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