Mobile edge computing (MEC) is a promising paradigm to meet the quality of service (QoS) requirements of latency-sensitive IoT applications. However, attackers may eavesdrop on the offloading decisions to infer the edge server's (ES's) queue information and users' usage patterns, thereby incurring the pattern privacy (PP) issue. Therefore, we propose an offloading strategy which jointly minimizes the latency, ES's energy consumption, and task dropping rate, while preserving PP. Firstly, we formulate the dynamic computation offloading procedure as a Markov decision process (MDP). Next, we develop a Differential Privacy Deep Q-learning based Offloading (DP-DQO) algorithm to solve this problem while addressing the PP issue by injecting noise into the generated offloading decisions. This is achieved by modifying the deep Q-network (DQN) with a Function-output Gaussian process mechanism. We provide a theoretical privacy guarantee and a utility guarantee (learning error bound) for the DP-DQO algorithm and finally, conduct simulations to evaluate the performance of our proposed algorithm by comparing it with greedy and DQN-based algorithms.

翻译：移动边缘计算是一种有前景的范式，能够满足延迟敏感的物联网应用的服务质量要求。然而，攻击者可能通过窃听卸载决策来推断边缘服务器的队列信息和用户的访问模式，从而引发模式隐私问题。因此，我们提出了一种卸载策略，该策略在保护模式隐私的同时，联合优化延迟、边缘服务器的能耗和任务丢弃率。首先，我们将动态计算卸载过程建模为马尔可夫决策过程。接着，我们开发了一种基于差分隐私深度Q学习的卸载算法（DP-DQO），通过向生成的卸载决策中注入噪声来解决该问题并应对模式隐私挑战。这通过使用函数输出高斯过程机制修改深度Q网络（DQN）来实现。我们为DP-DQO算法提供了理论上的隐私保证和效用保证（学习误差界），最后通过仿真实验，将所提算法与贪婪算法和基于DQN的算法进行对比，评估其性能。