  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.
