## 2 Background and Related Works
### Related Works on Privacy Protection  
To address the problem that adversaries can judge terminal usage patterns by capturing and analyzing the statistical characteristics of encrypted traffic, two types of privacy protection solutions are explored. They are (1) schemes that confuse the adversary by perturbing the statistical characteristics of the traffic, and (2) schemes that confuse the adversary by deliberately adjusting the offloading policy, respectively.  
The first scheme refers to generating noise that can perturb the packet size and packet interval of traffic by exploiting differential privacy [18; 19; 20] techniques. In this way, the adversary will hardly analyze terminals usage pattern privacy according to the statistical characteristics of the captured traffic. [PERSON] et al. [21] first explored the effectiveness of this scheme. They implemented three noise-generating differential algorithms, including the Fourier perturbation algorithm (\(FPA_{k}\)), \(d^{*}\)-private mechanism (\(d^{*}\)), and \(d_{L1}\)-private mechanism (\(d_{L1}\)) to perturb video stream features. Specifically, \(FPA_{k}\) answers long query sequences over correlated time-series data in a differentially private manner based on the discrete Fourier transform (DFT). \(d^{*}\) extends the differential privacy mechanism from [34] and applies Laplacian noise on time-series data. \(d_{L1}\) achieves differential privacy with regard to the L1 distance. Their experimental results show that all these three noises can reduce the accuracy of adversaries in obtaining privacy information by analyzing encrypted traffic. But this scheme is more suitable for situations with good channel quality. For satellite-terrestrial links with large radio fading, these noises may distort or invalidate during transmission.  
The second scheme refers to making the adversary misestimate the packet size by adjusting the offloading strategy. According to [PERSON] et al. [22], when the channel condition  
Figure 1: The usage pattern privacy leakage scenario.  
is good, the mobile device tends to offload all the generated and buffered tasks at each time slot to the MEC server. In this case, adversaries can extract statistical information and even patterns of each device's usage based on its task-offloading history. In response to this problem, they proposed a privacy-aware offloading approach. The main idea is to make the adversary misjudge the offload task stream size by deliberately processing a portion of tasks locally. They modeled the privacy protection strength and offloading overhead. Based on constrained Markov decision processes, the minimum overhead offloading scheme that satisfies the privacy protection strength can be calculated. This scheme can effectively confuse adversaries but cannot be performed by some sensing devices without computational capabilities since these devices in smart grids cannot execute tasks locally.  
Instead, in this paper, we consider a more general scheme to protect terminal usage pattern privacy. Specifically, terminals make the adversary misestimate the packet size by transmitting additional dummy tasks with random sizes when the real task is transmitted. This scheme does not require terminals to have computational capabilities; thus, it is more suitable for smart grid scenarios that have terminals with different computational power. Combined with specific network coding techniques [35], the dummy task packets are promising to compensate for packet loss in fast-fading satellite wireless links.