5G-Empowered Drone Networks in Federated and Deep Reinforcement Learning Environments
Usman Iqbal Ahmed, Jerry Chun‐Wei Lin, Gautam Srivastava · IEEE Communications Standards Magazine · 2021
Discovering important knowledge that may be available from databases while preserving the privacy of sensitive information is a hot research subject in data mining recently. With the establishment of 5G, several data-intensive applications will be developed. Privacy of information over the network is increasingly relevant, and as edge computing has grown more critical, applications running over drone networks require protection. The privacy of information while utilizing data is a trade-off that needs to be addressed. In this article, a deep-reinforcement-learning-based technique is applied to hide the sensitive information from a given database while keeping the balance between privacy protection and knowledge discovery during the sanitization process. Furthermore, minimizing known side effects that can be caused in the sanitization process is also considered. A particular set of recommendations, along with potential applications, are discussed with use cases.