This chapter highlights the criticality of data privacy in mental health as confidentiality and integrity of data are of paramount importance both for patients and doctors. With data privacy laws and regulations in place across the globe, specific to different countries, this chapter brings about clarity in incorporating privacy into advanced data and AI techniques while building mental health predictive algorithms. During the chapter, readers get an overview of the flaws in the measurement processes of data collected by the Internet of medical devices, along with potential risks of systems and algorithms designed for patients, care providers, and hospitals that could lead to data loss. In this context, this chapter also raises awareness of the significant implications of data leaks and security vulnerabilities when such leakage occurs and necessitating the urgency of safeguarding mental health information in a secured information, by and large for mental health apps (monitoring day-to-day activities of teenagers and youth) as well as for algorithms and services deployed at the cloud. The readers are further enlightened of the best practices of designing an algorithm with a code sample, along with a general understanding of the federated learning (FL) environment using aggregated mental health data. Further, by the end of this chapter, readers get a deeper understanding of how to safeguard psychological health.