d on specific requirements. In [237], an aggregative game was used to model SS in large-scale, heterogeneous, and dynamic network. By utilizing past channel access experience, an online learning algorithm was proposed to improve the utility of each user. Considering the heterogeneous impact of users, a multi-dimensional aggregative game was used to model the SS of the large-scale wireless network. A mediated privacy-preserving and truthful mechanism was developed to achieve an n-approximate ex-post NE and provided no regret guarantee for each user. It was shown that the proposed method satisfied joint differential privacy. 11.2.5.3 Privacy Protection for ML Algorithms The sensitive training data in ML-based applications faced distinct privacy issues. Malicious attackers can obtain private information through the structure of models or 11 Secure Spectrum Sharing with Machine Learning: Methodologies their observations. To investigate the privacy leakage of training data, the authors in [221] introduced novel formal definitions of advantage for membership and attribute inference attacks. Attacks in different learning algorithms and model properties were analyzed based on these definitions. It was shown that overfit could increase the risk of privacy leakages. Secure Multi Party Computation (MPC) allows different entities to share joint data to train their models without releasing any private information in the training data. The MPC-based privacy-preserving method was investigated in [134] for linear regression, logistic regression, and neural network training using the stochastic gradient descent method. A two-server model was considered, and the training data was securely dis- tributed among two non-colluding servers. Different models were trained on the joint data using secure Two Party Computation (2PC). It was shown that their new techniques could significantly increase speed while guaranteeing performance without leaking data privacy. Cloud computing frameworks provide many benefits to the communication networ