🤖 AI Summary
This work addresses the substantial computational overhead of existing homomorphic encryption schemes when processing high-resolution images. To mitigate this, the authors propose a multi-ciphertext privacy-preserving framework that enables parallel computation through image tiling and encrypted-domain convolution optimization via repeated packing. An efficient Sobel operator is specifically designed to support gradient computation on encrypted data. Key innovations include a tiling strategy that reduces ciphertext parameter size, a novel bootstrapping placement mechanism to minimize computational cost, and a sign-function-based polynomial approximation for reciprocal computation that enhances gradient direction accuracy. Experimental results demonstrate that the proposed approach significantly reduces the complexity of encrypting high-resolution images and computing their gradients, while simultaneously improving both client-side efficiency and server-side processing performance.
📝 Abstract
With growing emphasis on privacy protection, homomorphic encryption (HE) has emerged as a core method for privacy-preserving image processing, as it enables operations directly on encrypted data. However, existing research predominantly focuses on low-resolution image processing, and techniques for privacy-preserving high-resolution image processing remain underexplored. As the image size increases, the HE parameters must be adjusted accordingly, and directly applying existing methods can lead to significant computational overhead. In this work, we propose a multi-ciphertext privacy-preserving framework for large images, enabling efficient image encryption and computation under the semi-honest model. Specifically, we divide the large image into multiple sub-images, which allows us to maintain smaller HE parameters and reduce key size. By parallel processing the sub-image ciphertexts and introducing a new bootstrapping placement strategy, we significantly reduce encryption overhead and enhance user experience. On the server side, we optimize the large image convolution operation through a repeated packing technique and implement the Sobel operator computation based on HE. To improve gradient direction calculation for the Sobel operator, we introduce a new polynomial approximation method for the reciprocal function based on the sign function, which can be applied to other HE-based protocols.