## 3 Security and Privacy-Preserving Solutions for IoE
### Integration of Different Security Layers
#### 3.5.3 Proposed Integrated Layered Architecture
It provides long-term data security against quantum attacks through lattice-based cryptography [33; 34; 35], hash-based digital signatures [52; 53; 54; 55], and ZKP [48; 49; 77; 78; 79]. This layer does not actively produce data but cryptographically protects incoming data [69; 70; 120; 127]. _Quantum AI Analyzer_: This is the top analysis layer of the system. It takes Immutable Logs and Access Records from the blockchain layer and analyzes them using Quantum AI algorithms (e.g., Quantum-Enhanced ML, Quantum Support Vector Machine, QAOA) [127; 129; 130; 135; 136]. As a result of these analyses, the system produces Threat Predictions, Proactive Alerts, and Pattern Classifications. These outputs enhance the system's defense capacity and help develop proactive security strategies against future attacks. The concretization and functionality of the proposed architecture depend on defining algorithmic processes that explain how each technology in each layer works and how they interact with each other. In this context, the algorithms presented below detail the operational logic of the layers defined in the figure and provide a fundamental reference for potential real-world applications of the proposed system. These algorithms are conceptualized as a prototype-level approach, each representing the data flows, decision mechanisms, and security processes in the corresponding layer. By systematically structuring the input-output data and processing steps, these algorithms offer a critical roadmap for the practical implementation of the architecture. Algorithm 1: _AI-Based Pre-Processing of IoE Data:_ The purpose of this algorithm is to classify the raw data (S_i) coming from IoE devices and to make it meaningful for the next layer. ```
1:\(S\_i\) //Raw data from the IoE device M_AI //Trained AI model
2:Risk_Label: [Low, Medium, High], Filtered_Data
3: Receive data stream S_i from IoE device
4:\(X\_i\leftarrow\) preprocess(\(S\_i\))
5:\(threat\_features\leftarrow\) ExtractFeatures(\(X\_i\))
6:\(threat\_score\leftarrow\) M_AI(\(threat\_features\))
7:ifthreat_score \(\geq\) 0.8 then
8:Risk_Label \(\leftarrow\)\"High\"
9:elseif Threat_Score \(\geq\) 0.5 then
10:Risk_Label \(\leftarrow\)\"Medium\"
11:else
12:Risk_Label \(\leftarrow\)\"Low\"
13:endif
14:Filtered_Data \(\leftarrow\) summarize(\(X\_i\), threat_features)
15:returnRisk_Label, Filtered_Data
```  
**Algorithm 1**_AI-Based risk labeling for IoE streams_  
This algorithm processes the raw data received from IoE devices in real-time, converting it into an analyzable form. Initially, data from sensors is collected and passed through a preprocessing phase. In this phase, operations such as noise filtering, missing value completion, and normalization may be performed. Subsequently, AI-based analysis is conducted. This analysis is typically performed using pre-trained models (M_AI) or edge AI components. Data samples are classified into categories such as risky, sensitive, or ordinary. These labels contain information that shapes the system's security policy, including data sensitivity levels or potential anomaly risks. Finally, only the labeled and meaningful data is forwarded to the next layer (edge privacy and FL) for deeper analysis. In this way, the system eliminates unnecessary load while prioritizing security. Algorithm 2: Federated Learning Based Privacy-Preserving Training: The purpose of this algorithm is to perform local model training on distributed edge devices and update the global model without sharing user data. This algorithm enables AI models trained locally on edge devices or IoE nodes to learn without sending data to a central server. Initially, edge nodes receive the labeled data. Each device conducts model training on its own data. At the end of the training process, model weights (e.g., weight matrices, learning coefficients) are extracted. Before these model updates are sent to the blockchain, they pass through a privacy check.