## 1 Introduction
Thirdly, existing works rarely notice the problem that the task-offloading process will lead to privacy leakage, especially in the more vulnerable satellite network. Like any other wireless link, satellite links are also open and exposed but with less protection. Malicious adversaries can easily monitor computing task traffic by listening to satellite links. Utilizing machine learning technology, they can identify which computing tasks originate from which applications, and even what operations are performed by terminals [17]. Then, adversaries may initiate attacks specifically targeting terminals that are responsible for core services (e.g., adjustment control commands) in smart grids, which will cause large-scale power failures. Two schemes can be used to deal with such a privacy problem, but both of them are not applicable. For one thing, the differential privacy-based schemes [18; 19; 20; 21] are effective but may be distorted or invalidated over satellite-terrestrial links with large radio fading. For another, privacy protection schemes based on adjusted offloading strategies [22] ask terminals to be fully equipped with computational capabilities, and thus cannot directly apply to smart grids with different computational capabilities. To tackle the above problems, we present a novel task-offloading scheme with privacy protection consideration for smart grids in the satellite-terrestrial edge computing network. The main contributions of this paper are summarized as follows:(1) Satellite-terrestrial cooperative offloading architecture. In order to alleviate the queuing waiting delay of remote grids' tasks in the SatEC network, we consider a satellite-terrestrial cooperative edge computing architecture. In this architecture, the tasks can be executed by either SatEC server or urban TC, which effectively compensates for the low processing ability of pure satellite networks. A deep reinforcement learning-based task-offloading (DRTO) algorithm is proposed to decide where the task is being executed according to the current channel states (see Section 3.1). (2) Threat scenarios regarding privacy leakage. We provide a possible privacy leakage threat scenario during offloading in satellite-terrestrial cooperative networks, termed usage pattern privacy leakage. To protect against the privacy leakage problem,we consider transmitting some extra dummy tasks with a random size to confuse adversaries. To the best of our knowledge, we are the first to notice the privacy leakage problem in satellite-terrestrial cooperative offloading (see Section 2.2). (3) Offloading cost model with privacy consideration. We quantify the overhead caused by privacy protection. And the total offloading cost is modeled as a weighted sum of the latency, energy consumption, and privacy overhead. The process of solving the optimal offloading location decision and bandwidth allocation that minimizes the offloading cost is formulated as a MIP problem (see Section 3.4). (4) Model-free learning. To solve the above MIP problem, we propose a low-complexity deep reinforcement learning-based task-offloading (DRTO) algorithm. Considering that satellite trajectories are difficult to acquire, the proposed DRTO algorithm makes offloading decisions only based on the current channel states. Meanwhile, DRTO can improve its offloading policy by learning from the real-time trend of channel states, which adapts to the high dynamics of satellite-terrestrial networks (see Section 4). (5) Low time complexity. Compared with traditional optimization methods, DRTO completely removes the need for solving hard MIP problems. Furthermore, we dynamically adjust the size of the action space to speed up the learning process. Simulation results show that the runtime consumption of DRTO is significantly decreased, while the offloading cost performance is not compromised (see Section 5). We note that a shorter version of this paper was presented in the WCNC conference [23]. Our initial conference paper did not notice the privacy leakage problem during offloading.