## 3 Related Work  
Security and privacy in healthcare applications are highly sensitive because of personal information and health details that necessitate secure access, minimal information disclosure, and high accuracy. However, in [2], the authors conducted a year-long analysis of popular mobile health applications, revealing major shortcomings, including data protection violations, lack of encryption, insecure data transmission, and improper programming practices, risking user privacy by exposing health conditions, personal details, and location data to third parties. Also, works like [1; 6] have found significant gaps in user data protection. Key findings indicated that a substantial percentage of healthcare applications may collect and share user data with third parties, highlighting the urgent need for clearer communication and improved regulations in the health application market to protect users.  
Although there are numerous digital applications that offer healthcare e-services and personalized healthcare experiences, there is a notable lack of effort to ensure compliance with GDPR, which prioritizes user privacy and data protection. The research in [7] indicates that significant data privacy issues persist, such as unnecessary permissions, insecure cryptographic implementations, and the leakage of personal data and credentials in logs and web requests. Furthermore, the risk of user profiling remains high, as many applications fail to implement robust mechanisms to prevent linkability, detectability, and identifiability. One potential solution to these challenges could involve the use of PETs, as they provide a range of features for protecting users' personal information and sensitive data from unauthorized access and tracking [8].  
Furthermore, to enhance personalized experiences in healthcare applications, machine learning algorithms are increasingly used to analyze user data and deliver tailored outcomes. However, these techniques also raise significant privacy concerns [9]. They can expose sensitive patterns in the data, which may be exploited by malicious actors to cause financial or social harm, or by corporations to infer personally identifiable or confidential health information for commercial purposes [9; 10]. To address these risks while still preserving personalization, Federated Learning (FL) has emerged as a promising solution. FL enables collaborative model training across distributed devices or institutions without transferring raw data, thereby minimizing exposure and enhancing data security [11].  
Despite extensive research on privacy in healthcare, a critical gap persists: no unified system has been implemented that holistically addresses the operational needs of healthcare organizations while reconciling patient and clinician requirements with modern privacy-preserving technologies. Existing solutions often fail to integrate essential components such as traditional e-services (e.g., secure authentication) with privacy-centric features like anonymous access, or they neglect the unique challenges of FL when applied to sensitive health data collected from smart devices--particularly in mental health contexts, where robust privacy protections are urgently needed but rarely deployed. Our work advances the state of the art by presenting a fully implemented system that bridges these gaps. The system combines conventional e-health services with privacy-enhancing functionalities, including anonymous access and FL-based model training, while rigorously evaluating the trade-offs between model accuracy, security, and privacy preservation. By demonstrating practical performance through real-world implementation and compliance with GDPR and privacy-by-design principles, we provide a scalable solution that aligns with both clinical workflows and patient expectations, setting a new benchmark for deployable privacy-preserving healthcare systems.