Homomorphic encryption (HE) enables computations on encrypted data without decryption, a cornerstone for privacy in the cloud and secure artificial intelligence. However, its application to deep neural networks remains challenging due to the enormous computational overhead. Structured pruning has become an essential technique to reduce the operational burden, but until now no approach had considered the impact of pruning on model reliability under HE. This article presents an original analysis and proposes PSAP (Polynomial-Sensitivity-Aware Pruning), a pruning method that integrates polynomial sensitivity and rotation cost together with weight magnitude, achieving an unprecedented balance between efficiency and robustness against encryption errors.
The motivation behind PSAP is clear: in HE environments, bit-flip errors (both in the encryption layer and during computation) can completely destabilize inference. Traditional methods, such as magnitude pruning, remove weights based on their absolute value without considering how those weights interact with polynomial activations and the rotations required for ciphertext vector multiplication. As a result, errors can concentrate in critical layers causing catastrophic accuracy drops (>10 percentage points). PSAP scores each filter by combining three criteria: weight magnitude (numerical importance), polynomial activation sensitivity (how much an error in that filter affects the output), and Halevi–Shoup rotation cost (which determines operation complexity). This way, pruning concentrates on fault-tolerant regions, leaving the most fragile layers intact.
Experiments conducted on two architectures (similar to ResNet-32 and LeNet), two datasets, two numerical representations, and five bit-error rates (BER) confirm PSAP's superiority. While magnitude-pruned models exhibit between 5 and 14 vulnerable layers with catastrophic losses, PSAP limits that number to a maximum of two. Under controlled fault injection in 32-bit integer representation (int32), vulnerability reduction reaches up to 29 times. Additionally, direct fault injection analysis in CKKS encryption reveals a safe operating threshold around BER 10⁻⁵, validating the use of int32 as a conservative reliability proxy. Critically vulnerable structural layers account for only 1.1% of total parameters, allowing selective hardening with minimal overhead.
From a computational efficiency perspective, PSAP reduces Halevi–Shoup rotations by up to 45.2% on the more complex architecture. Furthermore, an adaptive mixed-degree assignment scheme lowers multiplicative depth from 66 to 56 levels, enabling leveled inference without bootstrapping. This represents a direct advance for practical HE deployments in the cloud, where response time and computation cost are limiting factors.
For businesses seeking to adopt privacy-preserving artificial intelligence, these results open the door to safer and more efficient deployments. Combining reliable pruning with homomorphic encryption allows processing sensitive data —such as medical records, financial transactions, or customer data— without exposing information. However, implementing these techniques requires deep knowledge of both cryptography and neural network architecture, as well as the supporting cloud infrastructure. That is where a specialized technology partner makes the difference.
Q2BSTUDIO is a software and technology development company offering advanced solutions in artificial intelligence, cybersecurity, and cloud computing. Its engineering team masters the integration of homomorphic encryption and optimized pruning techniques, helping organizations build custom AI agents that operate on encrypted data and cybersecurity systems that protect cloud inference. Q2BSTUDIO turns theory into robust enterprise solutions.
Additionally, the company provides consulting and development services in cloud AWS/Azure, business intelligence with Power BI, and process automation, all aligned with the scalability and resilience needs of modern applications. For example, a financial recommendation system using HE with PSAP pruning can be deployed on a cloud infrastructure managed by Q2BSTUDIO, reducing operational costs and ensuring user data confidentiality. The combination of reliable pruning and optimized cloud enables acceptable latencies even for complex models.
In conclusion, the reliability of encrypted models is not a secondary aspect but a critical requirement for their adoption in production environments. PSAP demonstrates that it is possible to prune neural networks with robustness guarantees against errors, and Q2BSTUDIO is ready to bring this technology to businesses. For organizations wishing to explore the potential of homomorphic encryption without compromising accuracy or efficiency, the path lies in applying reliability-aware pruning methods and relying on an expert team that integrates these capabilities into custom software applications. The future of secure AI is here, and it is built on intelligent pruning and technological collaboration.
