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.

翻译：卫星通信网络因其无缝覆盖与协作计算能力而受到广泛关注。在星地网络中，地面用户可将计算任务卸载至具备强大算力的可视卫星。现有卫星辅助任务计算方案通常聚焦于任务完成时间、能耗等系统性能优化。然而，由于卫星高速移动特性及不可靠的通信信道，现有方法仍面临严重的隐私泄露问题。本文提出一种面向动态移动特性的星地一体化网络架构，以支持卫星辅助任务卸载。同时，我们设计了一种隐私保护任务卸载方案，旨在弥合卸载性能与隐私泄露之间的差距。具体而言，我们平衡了两种卸载隐私类型——使用模式隐私与位置隐私，并兼顾不同卸载目标（如完成时间、能耗与通信可靠性）。最后，我们将问题建模为联合优化问题，并引入基于深度强化学习的隐私保护算法以获取最优卸载策略。实验结果表明，本算法在完成时间、能耗、隐私保护水平及通信可靠性方面均优于其他基准算法。我们希望该工作能为卫星辅助边缘计算中的隐私保护任务卸载提供更优解决方案。