The Social Internet of Things (SIoT) combined elements of the Social Networking aspect of the Internet of Things (IoT) allowing for connected devices to interact with each other, to share/consume data from and about each other, and to arrive at smart decisions. A key factor to making SIoT is the addition of Machine Learning (ML) to add value to SIoT’s richness by using predictive analytics, personalizing content, and automation. That being said, SIoT data is mostly sensitive data, and given the possible unauthorized access, adversarial threats, and legislation, using Privacy-Preserving Machine Learning (PPML) will be required. This chapter highlighted considerations of privacy related to SIoT including data sensitivity, risks/safety risk of data sharing, and adversarial threat. It also highlighted ethical and legal considerations within the context of a number of possible regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). It discussed elements to PPML framed as principles, demonstrated as a motivating example, and presented trade-offs to efficiency, privacy, and accuracy in sample practices accustomed to in relation to PPML and data privacy. It also described forms of privacy such as leading to what fluency to when it makes sense to use federated learning, homomorphic encryption, secure multi-party computation, differential privacy, blockchain applications, and incremental best recommendations/practices for privacy-preserving hybrid model options derived from data privacy. It outlined a proposed lifecycle for a PPML model in the SIoT model, including secure data collection and anonymity, privacy and ethical-aware training, deploying, and maintaining the PPML model. It also provided practical examples of implementations.