Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Sep 26, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

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

Evgenia Novikova¹, Dmitry Fomichov¹, Ivan Kholod¹

¹Faculty of Computer Science and Technology, Saint Petersburg Electrotechnical University "LETI", Saint Petersburg 197376, Russia.

Sensors (Basel, Switzerland)

|April 23, 2022

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

MFedBN: Tackling Data Heterogeneity with Gradient-Based Aggregation and Advanced Distribution Skew Modeling.

Sensors (Basel, Switzerland)·2025

Same author

Design and implementation of the first proton beam transport line in VEGA-3 Petawatt laser system.

Scientific reports·2024

Same author

Damage threshold of LiF crystal irradiated by femtosecond hard XFEL pulse sequence.

Optics express·2023

Same author

Cyber Attacker Profiling for Risk Analysis Based on Machine Learning.

Sensors (Basel, Switzerland)·2023

Same author

Laboratory evidence of magnetic reconnection hampered in obliquely interacting flux tubes.

Nature communications·2022

Same author

Privacy Policies of IoT Devices: Collection and Analysis.

Sensors (Basel, Switzerland)·2022

This summary is machine-generated.

Federated learning (FL) offers privacy for driver monitoring data but faces security challenges. Current privacy techniques in open-source frameworks limit practical FL applications, especially for real-time driver behavior analysis.

Area of Science:

Computer Science
Cybersecurity
Human-Computer Interaction

Background:

Wearable devices and smartphones collect sensitive driver data (audio, video, location, health).
Processing this data requires strict adherence to personal data security and privacy regulations.
Federated learning (FL) is a privacy-preserving paradigm, but lacks formal privacy guarantees and is vulnerable to attacks.

Purpose of the Study:

Analyze privacy-preserving techniques for FL in driver monitoring.
Compare implementations in open-source FL frameworks.
Evaluate the impact of privacy techniques on FL training efficiency and accuracy for driver behavior analysis.

Main Methods:

Comparative review and analysis of privacy-preserving techniques in open-source FL frameworks.

Keywords:

differential privacy driver activity monitoring federated learning homomorphic encryption open-source federated learning frameworks privacy secure multi-party computations

Related Experiment Videos

Last Updated: Sep 26, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Evaluation of technique impact on global model accuracy, training time, and network traffic.

Experimental setup focused on driver activity monitoring using smartphone sensor data.

Main Results:

Current privacy-preserving techniques in open-source FL frameworks have limitations.
These limitations significantly impact the practical application of FL for driver monitoring.
The overhead of privacy techniques affects training efficiency and resource utilization.

Conclusions:

Existing privacy techniques in FL frameworks are not yet fully suitable for real-time driver monitoring applications.
Further research is needed to enhance the efficiency and security of FL for sensitive data processing.
The practical application of FL is currently restricted to cross-silo settings due to these limitations.