## 1 Introduction  
Nowadays, technology contributes more and more to aid its users in carrying out challenging tasks by creating innovative applications. Especially in the healthcare sector, assistive technologies provide benefits of convenience and accessibility, collecting patients' healthcare data, helping doctors to provide better diagnoses, and helping caregivers to control patients' conditions even remotely via mobile devices. Health apps must adhere to the same level of privacy as the doctor-patient confidentiality regulation in medical visits. Users must rely on trust when it comes to the handling of personal information in apps and the services they connect to, ensuring a high level of usability for a seamless experience. This trust is based on the belief that app services are operated ethically, adhering to privacy regulations and security best practices [1]. According to [2], even popular applications fail to provide basic protection for users' privacy due to either inappropriate implementations or poor design choices. Data that is used in healthcare applications contains information such as their name, age, sex, and many other critical forms of identity information, in addition to the healthcare records that characterize the user's physical condition. Consequently, if this data was to be disclosed to a malicious entity, it would place the patient at significant risk regarding their security and privacy. Health applications are exposed to a multitude of vulnerabilities across various processes, ranging from user access control to communication between different stakeholders, and data processes throughout their lifecycle. Consequently, these reasons necessitate the adoption of Privacy-Enhancing Technologies (PETs) in health applications. PETs refers to a group of different techniques for enhancing both privacy and security [3]. Key characteristics and examples of PETs include data anonymization and pseudonymization, homomorphic encryption, privacy-preserving authentication mechanisms, Federated Learning, differential privacy, and blockchain technology. Despite advancements in PETs, many developments are still at the proof-of-concept stage, explaining why their adoption is not widespread, underscoring the need for further exploration of the barriers preventing their integration into the healthcare sector [3; 4; 5].Therefore, widespread adoption of PETs remains a challenge that must be addressed to ensure the future of secure digital healthcare solutions. In this work, we present a general framework regarding safeguarding health applications from security and privacy risks and develop it for a specific use case, particularly in our case, the concept of using apps to manage Attention Deficit Hyperactivity Disorder (ADHD) in children. Mental health apps deal with highly sensitive data, in contrast to other general health apps, e.g., for fitness and wellness [6]. Especially when these apps are dedicated to children, it is crucial to prioritize security and privacy features. Our ultimate goal is to provide a trustworthy and privacy-preserving solution to different actors involved in the gamified ADHD application, particularly children, parents, medical staff, and educators. We develop a highly efficient, scalable, and user-centric identity management framework that integrates advanced Privacy-Enhancing Technologies (PETs) with a Federated Learning (FL) architecture. This combined approach enables AI-driven predictions while strengthening data privacy, supporting patient autonomy, and overcoming the inherent limitations of centralized healthcare systems. In contrast to traditional models that rely on centralized models prone to single points of failure, our solution embraces a decentralized paradigm. By incorporating a blockchain-based identity management system, we ensure secure, tamper-resistant credential issuance and verification, fostering trust, resilience, and compliance with modern privacy regulations. This work makes several key contributions.