oblem was modeled as an MDP problem to protect the transmission 11 Secure Spectrum Sharing with Machine Learning: Methodologies data. A DQN-based approach was introduced to solve this MDP problem, and the e-greedy strategy was applied to balance the exploitation and exploration. A secure EE-based communications problem with energy harvesting ability in CRN was investigated in [48]. With the limited energy supply and presence of passive eavesdroppers, a TL actor-critic learning-based algorithm was introduced to help the SUs determine their operation mode to achieve a higher security level. In particular, SUs interact with the envi- ronment directly and choose to either stay idle to save energy or transmit the encrypted sensing results to FC by using a suitable private-key encryption method to maximize the long-term effective security level of the network. In real applications, some government agencies need to locate suspicious communica- tions via legitimate eavesdropping in an efficient manner. To this end, it is necessary to study the optimal attack strategies for energy-constrained eavesdroppers. A full-duplex active eavesdropper with a limited energy budget was considered in [184]. It sought to capture data and interfere with suspicious transmission links. A legitimate attack optimization problem was formulated based on a partially observable MDP framework to maximize the achievable wiretap rate while minimizing the suspicious throughput over a Rayleigh fading channel. Based on the available energy and beliefs regarding licensed channel activity, leavesdrop- pers needed to determine the course of action with maximum long-term system benefits. This may be either passive eavesdropping without jamming or active eavesdropping with an optimal amount of jamming energy. 11.2.5 State-of-the-Art ML-Based Privacy Protection Methods In this section, privacy protections for PU, SU, and ML in different SS frameworks are sequentially reviewed. 11.2.5.1 Privacy Protection for PUs in SS Networks In some SS networks such as SA