## 7 Results and Analysis
### Discussion
#### 7.12.3 Privacy Enhancements  
Figure 16 shows that at a privacy budget of \(\epsilon=2.0\), our adaptive DP mechanism preserves 92% accuracy, surpassing the 88% of the DP-FL baseline by [62]. Membership-inference attacks succeed only 12.3% of the time under our scheme (Figure 17), a 61% reduction relative to DP-FL, and well below the 30-40% commonly seen in the literature [63]. As Table 13 confirms, these privacy gains incur minimal overhead--3.8 ms per round and 8% extra computation--making our solution compatible with near-real-time O-RAN constraints. We analyze three enhanced privacy architectures that combine differential privacy (DP) with additional cryptographic techniques to achieve layered protection in FL environments. Table 20 presents a comparison of these techniques in terms of privacy guarantees, computational overhead, and best-fit deployment scenarios. The DP-only approach, using an \(\epsilon\) value of 2, offers lightweight privacy guarantees and is particularly suitable for resource-constrained edge nodes where computational capacity is limited. By combining DP with homomorphic encryption (HE), the framework achieves both \(\epsilon\)-DP and computational security; however, this combination incurs significant computational overhead, making it more applicable to scenarios involving highly sensitive model updates. Alternatively, integrating DP with Secure Multi-Party Computation (SMPC) enables both \(\epsilon\)-DP and information-theoretic security, with moderate overhead, and is especially well suited for multi-party critical systems where cross-operator collaboration is required. The hybrid privacy workflow illustrated in Figure 21 demonstrates the sequential combination of DP and HE. This approach first applies DP noise injection to raw gradients, followed by homomorphic encryption before secure aggregation. This order ensures that DP provides statistical protection upfront, thus avoiding amplification of privacy risks through encrypted operations, while HE protects the already DP-noised gradients throughout the aggregation process. As reflected in Table 20, this strategy results in a \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Technique** & **Privacy Guarantee** & **Overhead** & **Best Scenario** \\ \hline DP Only (\(\epsilon=2\)) & \(\epsilon\)-Differential Privacy & Low & Resource-constrained edge nodes \\ \hline DP + Homomorphic & \(\epsilon\)-DP + Computational Security & High & Sensitive model updates \\ \hline DP + SMPC & \(\epsilon\)-DP + Information-Theoretic Security & Medium & Multi-party critical systems \\ \hline \hline \end{tabular}
\end{table} 
Table 20: Privacy technique comparison. 3.2\(\times\) increase in computation overhead compared to DP alone but substantially enhances resilience against gradient leakage attacks. Furthermore, the system implements an adaptive, context-aware privacy selection mechanism based on available resources and data sensitivity. The selection logic, as defined in Equation (6), prioritizes DP-only methods when the residual energy budget (\(E_{\text{res}}\)) falls below 0.5, switches to DP + HE when the sensitivity score (\(S_{\text{crit}}\)) exceeds 0.7, and defaults to DP + SMPC in all other cases. This mechanism ensures that privacy strategies dynamically align with the system's operational constraints and data criticality levels. Implementation results confirm the effectiveness of the proposed combinations. Specifically, the DP + HE configuration reduced the success rate of gradient leakage attacks by 92%, outperforming DP-only approaches, which achieved a reduction of 78%. Additionally, DP + SMPC enabled secure four-party model training, maintaining an accuracy level of 89%, thereby validating its suitability for collaborative scenarios.