Enhancing Trajectory Recovery From Gradients via Mobility Prior Knowledge-Reference-Cited by-同舟云学术

Enhancing Trajectory Recovery From Gradients via Mobility Prior Knowledge

Published:2023-03-15 Issue:6 Volume:10 Page:5583-5594
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Zhang Kaiyue¹^ORCID,Fan Zipei²^ORCID,Song Xuan³^ORCID,Yu Shui⁴^ORCID

Affiliation:

1. Department of Computer Science and Engineering, SUSTech–UTokyo Joint Research Center on Super Smart City, Southern University of Science and Technology, Shenzhen, China

2. Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China

3. Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China

4. Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia

Funder

National Key Research and Development Project of China

Guangdong Provincial Key Laboratory

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10061500/09951065.pdf?arnumber=9951065

Reference52 articles.

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3. Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning

4. iDLG: Improved deep leakage from gradients;zhao;arXiv 2001 02610,2020

5. Federated Learning: Challenges, Methods, and Future Directions