S and LSA, leakage of PU's privacy can cause serious secu- rity threats. Potential malicious adversaries may exploit attacking technologies such as DIA to obtain information about the IU and that can later be used to develop attack strategies. To defend the DIA and protect IUs, one viable approach is to obfuscate the information revealed by the database. There are a lot of works using ML-based obfuscation techniques to counter inference attacks. The authors in [43] investigated whether or not a malicious opponent could infer the radar's location through veiled radar information contained in the system's precoder. An ML-based location inference attack was simulated for two specific precoder schemes. It was verified that radar privacy could be compromised by system information leaked through the precoder, introducing various degrees of risks. The tradeoff between privacy preservation and spectrum efficiency was examined in [36]. A generalized SS system architecture was proposed and a multi-utility user privacy opti- mization problem was formulated. Potential adversary inference attacks were used to measure privacy, and an efficient heuristic strategy was developed to solve the problem. Compared with existing obfuscation strategies, the proposed approach can achieve a 50% increase in privacy with an insignificant impact on SE. 11.2 ML-Assisted Secure SS In [187], the authors first showed that adversarial SUs could employ a Bayesian learning-based inference algorithm to accurately locate a non-stationary radar system using only information gathered from seemingly innocuous query replies obtained from a SAS. Several obfuscation techniques were then proposed and implemented in the SAS for countering such inference attacks. Finally, the obfuscation techniques' efficacy in minimizing spectral efficiency loss while preserving incumbent privacy was investigated. The authors in [202] proposed a CS-based federated learning framework to achieve IU detection for improving communication efficiency while protecting the privacy o