Optimal privacy preservation strategies with signaling Q-learning for edge-computing-based IoT resource grant systems

Data privacy leakage can be severe when a malicious Internet of Things (IoT) node sends requests to gather private data from an edge-computing-based IoT cloud storage system across the edge nodes. To solve this problem, a privacy-preservation signaling game for edge-computing-based IoT networks is proposed to characterize the interactions between an IoT node and its corresponding edge node when managing an IoT resourcegrant system. Optimal privacy preservation strategies for edge nodes are then theoretically derived. A signaling Q-learning algorithm is then designed to address the problem of achieving convergent equilibrium and game parameters from a practical perspective. The theoretical results are validated using simulations that focus on two statistical points (i.e., the optimal probability of an IoT node requesting maliciously and the posterior probability of an IoT node being malicious). By comparing the proposed signaling Q-learning algorithm with the greedy algorithm benchmark, the proposed algorithm is shown to more effectively decrease the optimal probability of an IoT node sending malicious requests. Thus, privacy preservation for edge-computing-based IoT cloud storage systems can be strengthened.

Automated summary

This study proposes a signaling game approach for privacy preservation in edge-computing-based IoT networks. It addresses the issue of malicious IoT nodes requesting private data from an IoT cloud storage system across edge nodes. The optimal privacy preservation strategies for edge nodes are derived and a signaling Q-learning algorithm is designed to achieve convergent equilibrium and game parameters. Simulation results show that the proposed algorithm effectively decreases the optimal probability of malicious requests, enhancing privacy preservation in edge-computing-based IoT cloud storage systems.