Future Research Directions

From Chaps. 2–5, we have shown the existing research status of machine learning driven privacy preservation in IoTs, especially focus on three leading directions using GAN, federated learning, and reinforcement learning. Several advanced technologies and theories are also integrated, for example, differential privacy, game theory, blockchain, etc. Nevertheless, there are still plenty of significant and prospective issues worthy investigating. The popularization of blockchain, digital twin, and artificial intelligence offers a mass of opportunities for researches on machine learning driven privacy preservation in IoTs, but meanwhile they raise new challenges such as unsatisfying data utility and limited communication and computing resources. In addition, there are various other research topics that desiderata consideration in machine learning driven privacy preservation in IoTs. To pave the way for readers and forthcoming researchers, we outline several potentially promising research directions that may be worthy of future efforts.