Secure Nonlocal Denoising in Outsourced Images

Abstract

Signal processing in the encrypted domain becomes a desired technique to protect privacy of outsourced data in cloud. In this article, we propose a double-cipher scheme to implement nonlocal means (NLM) denoising in encrypted images. In this scheme, one ciphertext is generated by the Paillier scheme, which enables the mean filter, and the other is obtained by a privacy-preserving transform, which enables the nonlocal search. By the privacy-preserving transform, the cloud server can search the similar pixel blocks in the ciphertexts with the same speed as in the plaintexts; thus, the proposed method can be executed fast. To enhance the security, we randomly permutate both ciphertexts. To reduce the denoising complexity caused by random permutation, a random NLM method is exploited in the encrypted domain. The experimental results show that the quality of denoised images in the encrypted domain is comparable to that obtained in the plain domain.