Deep Reinforcement Learning for Privacy-Preserving Task Offloading in Integrated Satellite-Terrestrial Networks

• Main Authors: Lan, Wenjun, Chen, Kongyang, Li, Yikai, Cao, Jiannong, Sahni, Yuvraj
• Format: Journal Article
• Language: English
• Published: 20-06-2023
• Subjects: Computer Science - Cryptography and Security; Computer Science - Networking and Internet Architecture
• DOI: 10.48550/arxiv.2306.17183

IEEE Transactions on Mobile Computing, 2024 Satellite communication networks have attracted widespread attention for
seamless network coverage and collaborative computing. In satellite-terrestrial
networks, ground users can offload computing tasks to visible satellites that
with strong computational capabilities. Existing solutions on
satellite-assisted task computing generally focused on system performance
optimization such as task completion time and energy consumption. However, due
to the high-speed mobility pattern and unreliable communication channels,
existing methods still suffer from serious privacy leakages. In this paper, we
present an integrated satellite-terrestrial network to enable
satellite-assisted task offloading under dynamic mobility nature. We also
propose a privacy-preserving task offloading scheme to bridge the gap between
offloading performance and privacy leakage. In particular, we balance two
offloading privacy, called the usage pattern privacy and the location privacy,
with different offloading targets (e.g., completion time, energy consumption,
and communication reliability). Finally, we formulate it into a joint
optimization problem, and introduce a deep reinforcement learning-based
privacy-preserving algorithm for an optimal offloading policy. Experimental
results show that our proposed algorithm outperforms other benchmark algorithms
in terms of completion time, energy consumption, privacy-preserving level, and
communication reliability. We hope this work could provide improved solutions
for privacy-persevering task offloading in satellite-assisted edge computing.