Privacy-Preserving Image Scaling Using Bicubic Interpolation and Homomorphic Encryption

Donger Mo, Peijia Zheng, Yufei Zhou, Jingyi Chen, Shan Huang, Weiqi Luo, Wei Lu, Chunfang Yang

Abstract: With the advancement of cloud computing, outsourced image processing has become an attractive business model, but it also poses serious privacy risks. Existing privacy-preserving image scaling techniques often use secret sharing schemes or the Paillier cryptosystem to protect privacy. These methods require the collaboration of multiple servers or only support additive operations, which increases the difficulty of data storage and complicates image processing. To address these issues, this paper focuses on cloud-based privacy-preserving image scaling in the encrypted domain. We propose an image scaling scheme based on homomorphic encryption, which allows cloud servers to perform scaling operations on encrypted images using bicubic interpolation. To avoid the high storage and communication costs of per-pixel encryption, we introduce an efficient data encoding method where a single ciphertext contains the information of an entire image. This significantly reduces the storage space and communication overhead with the cloud server, while our scheme supports computational operations in this data format. Our experimental results validate the feasibility of the proposed scheme, which outperforms existing schemes in terms of storage overhead and operational efficiency.