# Empirical Study of Fully Homomorphic Encryption Using Microsoft SEAL
## 4 Results
Finally, for larger datasets (1000 and 10,000 samples), the execution time on the Jetson Nano escalated dramatically, reflecting the device's constraints in handling intensive cryptographic operations. The device struggled with the largest datasets, especially at higher _poly_modulus_degree_ values, illustrating the limitations of edge devices in executing complex homomorphic encryption tasks without optimization. The investigation extended to CPU load and system utilization, revealing the Jetson Nano's operational thresholds. Similar to the previous experiments, initial tests with smaller datasets and lower _poly_modulus_degree_ values demonstrated minimal CPU strain, with system utilization remaining within acceptable ranges. This indicated the Nano's capability to handle basic homomorphic encryption tasks efficiently. When progressing to medium-sized datasets, an increase in CPU load was observed, indicating heightened resource demand. However, the device managed to complete the tasks, albeit with increased  
Figure 5: Average execution time for experiments run on a Jetson Nano. Experiments were run five times for each configuration. execution times. Then, at the upper values of dataset size and _poly_modulus_degree_, the Jetson Nano's CPU load reached its zenith, often hitting 100% utilization. This was particularly evident for the largest datasets, where the device occasionally failed to complete the encryption process due to exhaustive memory consumption and processing power limitations. The results are shown in Tables 1 and 2. An interesting point to note is how the occasional failures noted in the experiments on the Jetson Nano under high loads and large data sizes could lead to potential vulnerabilities from a security-application perspective. We have seen that these failures occur because of the limited memory capacity, resulting in the inability to complete the encryption task. Consequently, attackers could deliberately target systems using similar edge computing devices with high-load tasks to induce failure, causing a denial of service through resource exhaustion. Another risk would be the exposition of unencrypted data, or partially encrypted data, when the encryption process is incomplete or failed. Finally, it is important to observe that continuous operation near resource limits could lead to system instability, which might be exploited to disrupt operations or to force systems into a less secure state. A comparative analysis between the PC-based experiments and those conducted on the Jetson Nano sheds light on the scalability and adaptability of homomorphic encryption technologies across different computing environments. This comparison is crucial for understanding the practical implications of deploying advanced cryptographic solutions within both traditional and edge computing frameworks. In this sense, the Jetson Nano exhibited good performance with smaller datasets, often matching or slightly outperforming the PC setup in terms of execution time. This efficiency underscores the Nano's potential for handling lightweight encryption tasks effectively, despite its constrained computational resources. However, as the dataset size increased to medium levels, the execution time on the Jetson Nano began to lag significantly compared to the PC, highlighting the impact of its limited processing capabilities on handling more complex cryptographic computations. The disparity in execution time became more pronounced with larger datasets. The PC maintained a relatively stable performance curve, managing to complete encryption tasks with considerable efficiency even as the complexity of operations increased. Conversely, the Jetson Nano's performance degraded severely under the weight of larger datasets, particularly at higher _poly_modulus_degree_ values, illustrating the challenges faced by edge devices in executing computationally intensive homomorphic encryption tasks without specific optimizations.