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

arXiv: https://arxiv.org/abs/2302.04608 · HTML: https://mlsanity.com/paper/2302.04608/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 9 Feb 2023.

## Builds on (most cited first, 8 of 8)

- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [The Composition Theorem for Differential Privacy](https://mlsanity.com/paper/1311.0776/) (2013)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [Privacy-preserving Q-Learning with Functional Noise in Continuous State Spaces](https://mlsanity.com/paper/1901.10634/) (2019)
- [How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning](https://mlsanity.com/paper/1904.11082/) (2019)
- [DMRO:A Deep Meta Reinforcement Learning-based Task Offloading Framework for Edge-Cloud Computing](https://mlsanity.com/paper/2008.09930/) (2020)
- [Offloading and Resource Allocation with General Task Graph in Mobile Edge Computing: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2002.08119/) (2020)
- [A Cooperative Architecture of Data Offloading and Sharing for Smart Healthcare with Blockchain](https://mlsanity.com/paper/2103.10186/) (2021)

## Abstract (arXiv)

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.