thod, publicly shared dataset information describes the patterns of groups within the dataset but withholds information about individuals. In homomorphic encryption, the operation on encrypted data can be used to secure the learning process by computing on encrypted data. When user-generated data are dis- tributed among different data owners, SFE can enable multiple parties to collaboratively compute an agreed-upon function without leaking input information regarding any party other than what can be inferred from the output. ML-Assisted Secure SS In this section, the latest research in ML-related security will be comprehensively surveyed. The contents are organized as in Figure 11.2. Details of existing works will be reviewed in each category. 11.2.1 State-of-the-Art Methods of Defense Against PUE Attack In this section, different ML-based PUE attacker detection methods in [4, 12, 50, 52, 60, 86] will first be presented. To address the limitations of training data and inconsistent commu- nication environments, the robust detection methods in [35, 125, 162, 168] will be further discussed. Finally, the ML-based attack strategies included in [37, 38, 156] will be discussed to provide an attacker's perspective on PUE defense method design. 11.2.1.1 ML-Based Detection Methods A typical PUE attack is illustrated in Figure 11.3. In defending against such attacks, the most important step is distinguishing malicious attackers from legitimate PUs. This can be achieved using specific features extracted from received signals. Distinct features may reflect the transmitters' characters, rendering them unique and differentiable. 11.2 ML-Assisted Secure SS ML based Robust ML based detection detection attack methods methods methods Outlier Reputation SSDF and detection detection methods methods combination Attacker ML based enhanced based methods methods methods theory RL based based based methods methods methods Privacy Privacy Privacy protection protection protection for for PU for SU Figure 11.2 ML-assisted secure SS. Figure 1