Enabling Practical and Privacy-Preserving Image Processing
<|reference_start|>Enabling Practical and Privacy-Preserving Image Processing: Fully Homomorphic Encryption (FHE) enables computations on encrypted data, preserving confidentiality without the need for decryption. However, FHE is often hindered by significant performance overhead, particularly for high-precision and complex data like images. Due to serious efficiency issues, traditional FHE methods often encrypt images by monolithic data blocks (such as pixel rows), instead of pixels. However, this strategy compromises the advantages of homomorphic operations and disables pixel-level image processing. In this study, we address these challenges by proposing and implementing a pixel-level homomorphic encryption approach, iCHEETAH, based on the CKKS scheme. To enhance computational efficiency, we introduce three novel caching mechanisms to pre-encrypt radix values or frequently occurring pixel values, substantially reducing redundant encryption operations. Extensive experiments demonstrate that our approach achieves up to a 19-fold improvement in encryption speed compared to the original CKKS, while maintaining high image quality. Additionally, real-world image applications such as mean filtering, brightness enhancement, image matching and watermarking are tested based on FHE, showcasing up to a 91.53% speed improvement. We also proved that our method is IND-CPA (Indistinguishability under Chosen Plaintext Attack) secure, providing strong encryption security. These results underscore the practicality and efficiency of iCHEETAH, marking a significant advancement in privacy-preserving image processing at scale.<|reference_end|>
@article{wang2024enabling,
    title={Enabling Practical and Privacy-Preserving Image Processing},
    author={Chao Wang, Shubing Yang, Xiaoyan Sun, Jun Dai, Dongfang Zhao},
    journal={arXiv preprint arXiv:2409.03568},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03568},
    primaryClass={cs.CR}
}