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

Shuying Gan, Marie Siew, Chao Xu, Tony Q.S. Quek

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題ICC 2023 - IEEE International Conference on Communications
主出版物子標題Sustainable Communications for Renaissance
編輯Michele Zorzi, Meixia Tao, Walid Saad
發行者Institute of Electrical and Electronics Engineers Inc.
頁面3578-3583
頁數6
ISBN(電子)9781538674628
DOIs
出版狀態Published - 2023
事件2023 IEEE International Conference on Communications, ICC 2023 - Rome, Italy
持續時間: 2023 5月 282023 6月 1

出版系列

名字IEEE International Conference on Communications
2023-May
ISSN(列印)1550-3607

Conference

Conference2023 IEEE International Conference on Communications, ICC 2023
國家/地區Italy
城市Rome
期間23-05-2823-06-01

All Science Journal Classification (ASJC) codes

  • 電腦網路與通信
  • 電氣與電子工程

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