FEnc$^2$: Unifying Data Packing for Efficient Private Inference via Convolution and Architecture-Aware Fragment Encoding
arXiv:2606.16359v1 Announce Type: cross Abstract: Fully Homomorphic Encryption (FHE) enables privacy-preserving machine learning but incurs extreme computational and memory overhead. These costs come not only from expensive low-level primitives, including Number Theoretic Transform (NTT), rotation, and key-switching, but also from inefficient ciphertext packing at the application level. Existing packing strategies typically preserve either neighboring data elements or feature grouping, but not both, leading to wasted ciphertext slots, excessive rotations, and inflated ciphertext counts. We pro
The increasing demand for privacy-preserving AI combined with the inherent computational challenges of Fully Homomorphic Encryption (FHE) necessitates research into efficiency improvements.
Efficient FHE is critical for deploying privacy-preserving machine learning at scale, which has implications across various sensitive data-driven sectors.
Optimized data packing methods could significantly reduce the computational and memory overheads associated with FHE, making it more practical for real-world applications.
- · Privacy-focused AI developers
- · Cloud computing providers (for lower resource usage)
- · Healthcare and financial sectors
- · FHE hardware accelerators
- · Traditional privacy-invasive AI models
- · Companies unable to adapt to FHE standards
This research directly addresses a key bottleneck in the practical deployment of Fully Homomorphic Encryption for machine learning.
Improved FHE efficiency could accelerate the adoption of privacy-preserving machine learning in sensitive applications, fostering new business models.
Widespread FHE adoption might standardize data privacy in AI, influencing regulatory frameworks globally and democratizing access to secure computation.
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