# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 5 Discussion and Implications  
The experimental results effectively demonstrate that quantum-resistant security and practical performance are not mutually exclusive objectives in the design of privacy-preserving cloud services. Our framework successfully integrates post-quantum and homomorphic cryptography into a workable system. However, our investigation highlights several crucial points that merit discussion. The SIMD scalability results indicate that our approach is particularly well-suited to batch processing scenarios. Medical imaging pipelines or surveillance systems that process many images in succession benefit most from the amortization of the initial setup costs, a common optimization technique in applied FHE systems [5]. While practical for single images, the framework's full throughput potential is best realized in these multi-transaction sessions. The performance itself is highly practical: with server-side operations completing in approximately 2.5 ms and client-side decryption in approximately 3.4 ms, even for 4096-pixel images, the individual cryptographic stages do not present a bottleneck. However, our layered design introduces necessary trade-offs. The primary overhead is borne by the client, which is responsible for managing both PQC and FHE cryptographic keys and for performing all encryption and decryption operations. The sequential nature of the protocol--establishing a secure channel, exchanging FHE materials, and then processing data--contributes to the overall latency, although our results show that the individual steps are highly efficient. When considering practical deployment, several infrastructure requirements must be carefully planned. Client devices need sufficient memory to handle the computationally intensive key generation process, with memory usage peaking at approximately 100 MB during initialization. This requirement is manageable for desktop and server applications but may pose challenges for resource-constrained mobile devices. The network bandwidth requirements are more substantial, involving a 34 MB one-time setup cost for transmitting the FHE public materials, followed by roughly 4 KB per image for the actual encrypted data transfer. On the server side, the computational requirements are surprisingly modest--standard CPU hardware with 2 GB RAM per concurrent session is sufficient, making the framework accessible to organizations without specialized cryptographic hardware. Integration challenges vary significantly depending on the target environment. Legacy systems often require substantial protocol adaptation since they were not designed with the communication patterns that FHE demands. Mobile deployments present unique opportunities and challenges: while the computational overhead can strain battery life, the key generation phase can be strategically performed during charging periods to minimize user impact. Enterprise deployments introduce additional complexity, particularly around key management, where integration with Hardware Security Modules (HSMs) becomes essential for meeting organizational security policies and regulatory requirements. Our framework introduces a significant but quantifiable privacy premium. When processing 1000 images, traditional cloud processing costs approximately USD 0.15 (split between USD 0.10 for compute resources and USD 0.05 for storage). Our privacy-preserving approach increases this cost to USD 0.40 ( in a cost analysis based on standard pay-as-you-go pricing for AWS EC2 (c5.large instance) and S3 data transfer as of Q1 2025), with the additional expense primarily driven by increased computation time (USD 0.25), unchanged storage costs (USD 0.05), and network overhead (USD 0.10). This represents roughly a 167% increase in processing costs, which, while substantial, may be justified in privacy-sensitive applications. The adoption drivers for such a system are compelling in today's regulatory environment.