Differentially Private Deep Q-Learning for Pattern Privacy Preservation in MEC Offloading

TL;DR

This paper introduces a differentially private deep Q-learning algorithm for MEC offloading that balances latency, energy, and privacy, providing theoretical guarantees and demonstrating improved performance through simulations.

Contribution

It proposes a novel DP-DQO algorithm that injects noise into offloading decisions to preserve pattern privacy while optimizing multiple MEC performance metrics.

Findings

The DP-DQO algorithm achieves theoretical privacy and utility guarantees.

Simulation results show improved performance over greedy and standard DQN algorithms.

The approach effectively balances latency, energy consumption, and privacy in MEC offloading.

Abstract

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.