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

TL;DR

This paper introduces a deep reinforcement learning approach for privacy-preserving task offloading in satellite-terrestrial networks, balancing performance metrics with privacy concerns amidst high mobility and unreliable channels.

Contribution

It proposes a novel privacy-preserving offloading scheme that balances usage pattern and location privacy, formulated as a joint optimization problem solved by deep reinforcement learning.

Findings

Outperforms benchmark algorithms in completion time and energy consumption.

Enhances privacy preservation without sacrificing communication reliability.

Demonstrates effectiveness in dynamic satellite-terrestrial environments.

Abstract

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.