## 3 Security and Privacy-Preserving Solutions for IoE
### Integration of Different Security Layers
#### 3.5.3 Proposed Integrated Layered Architecture  
In modern systems, the proliferation of IoE devices has made it possible to monitor and process patient data in real time. However, this situation also brings multi-layered threats, including data security, privacy, access control, and system reliability. In such a complex environment, ensuring security cannot be accomplished through a single technology. Instead, a task-oriented integration of various approaches, including AI, Edge Computing, Blockchain, and Quantum-Resilient Cryptography, is required. In this context, Figure 10 illustrates a layered and integrated security architecture that covers the entire process in IoE-based systems, from data generation to threat analysis. This structure not only represents an academic model but also offers a multi-component solution framework for real-world applications. Figure 10: Proposed integrated layered architecture for secure and privacy-preserving data processing in IoE-based systems. This architecture offers a multi-layered solution to the security and privacy requirements emerging in IoE-based systems. From the moment data is generated, the processes of semantic interpretation, privacy-preserving processing, immutable storage, and future-oriented analysis are performed through sequential but complementary modules. Each layer not only fulfills its own function but also enables secure and meaningful data transfer to the upper layer, thereby building the holistic security of the system. As shown in Figure 10, the modules operate in an integrated manner, covering AI-based data analysis, edge-level privacy preservation, access-controlled blockchain records, and post-quantum-ready analytics processes. Below, the function of each layer, the methods used, and their role in the system are explained in detail. _IoE Devices_: This layer includes sensors, ECG devices, blood pressure monitors, wearable devices, and mobile health applications located in hospital environments. Raw patient data is generated at this point. This data is not directly suitable for processing and is vulnerable to security threats [13; 36; 42; 88]. _AI/ML-Based Detection Layer_: This is the first intelligent layer that analyzes raw data from IoE devices. Machine learning algorithms such as CNN, LSTM, Decision Trees, and Autoencoders are used for anomaly detection and adversarial attack detection. The system tags risky situations with risk labels and transmits the filtered data to the next layer [26; 31; 32; 59]. Edge Privacy-Preserving Computation: This layer processes the labeled data using privacy-preserving methods. The techniques used include:  
* _FL (Federated Learning)_: Model training is performed locally on edge devices, and data is not transferred to a central server [116; 137; 145; 146]. * _HE (Homomorphic Encryption)_: Encrypted data is processed directly [46; 104; 105]. * _DP (Differential Privacy)_: Individual data protection is added to cumulative data patterns [109; 110]. The resulting outputs are transmitted as encrypted/private outputs to the Blockchain layer. If the system operates with FL, model updates are also sent from this layer. The Quantum AI layer in the proposed architecture is conceptualized as a forward-looking module. While real-world quantum AI systems are still in experimental phases, this layer represents a visionary approach for future threat intelligence and security pattern analysis in IoE environments. _Blockchain (Smart Contracts + ABAC)_: The privacy-protected outputs are logged in this layer. Smart Contracts evaluate access requests using the ABAC model. Each transaction is immutably recorded, access is validated, and access control is enforced. Thus, only authorized individuals can access the data, and all transactions are securely stored in accordance with the principle of auditability [82; 83; 92; 97]. _Quantum-Resilient Cryptography_: This layer operates as if beneath the blockchain layer.