Prediction error expansion-based reversible data hiding in encrypted images with public key cryptosystem

Advances in signal processing in the encrypted domain and cloud computing have given rise to privacy-preserving technologies. In recent years, reversible data hiding in encrypted images (RDH-EI) has received attention from the research community because additional data can be embedded into an encrypted image without accessing its original content, and the encrypted image can be losslessly recovered after extracting the embedded data. Although the recent development of RDH-EI compatible with homomorphic public key cryptosystems has intensified research interest, most of the existing mature RDH schemes cannot be transplanted to the encrypted domain due to the limitations of the underlying cryptosystems. In this paper, prediction error expansion based RDH-ED using probabilistic and homomorphic properties of the Paillier cryptosystem is presented. This work implements non-integer mean value computation in the encrypted domain without any interactive protocol between the content owner and the cloud server. This work presents mathematical detail of pixel prediction (mean), prediction error, error expansion and data embedding in the encrypted domain and data extraction and content recovery in the plain domain. Experimental results from standard test images reveal that the proposed scheme outperforms other state-of-the-art encrypted domain schemes.