An Improved Separable and Reversible Steganography in Encrypted Grayscale Images

Reversible data hiding in encrypted images (RDH-EI) has attracted various communities that deal with security. Xiao et al. presented RDH-EI that uses pixel value ordering (PVO) and additive homomorphism for encryption ensuring that the embedding capacity of plain and encrypted image remains relatively same. However, the size of location map required to manage over/under flow blocks increased drastically reducing the overall embedding capacity. In the paper, a new PVO-based separable RDH scheme is proposed using double encryption. The original image is encrypted using chaotic mapping in Phase 1, ensuring that the performance of PVO algorithm in an encrypted domain is similar to that in plain domain. Secret data is embedded in this encrypted image. The embedded image is re-encrypted using additive homomorphism to enhance security. The proposed scheme is more efficient as it improves the pure embedding rate and total embedding capacity of the image by reducing location map size, overcoming the drawback of Xiao et al.

A Homomorphic Encryption and Privacy Protection Method Based on Blockchain and Edge Computing

With its decentralization, reliable database, security, and quasi anonymity, blockchain provides a new solution for data storage and sharing as well as privacy protection. This paper combines the advantages of blockchain and edge computing and constructs the key technology solutions of edge computing based on blockchain. On one hand, it achieves the security protection and integrity check of cloud data; and on the other hand, it also realizes more extensive secure multiparty computation. In order to assure the operating efficiency of blockchain and alleviate the computational burden of client, it also introduces the Paillier cryptosystem which supports additive homomorphism. The task execution side encrypts all data, while the edge node can process the ciphertext of the data received, acquire and return the ciphertext of the final result to the client. The simulation experiment proves that the proposed algorithm is effective and feasible.

Reversible Data Hiding with Pixel Prediction and Additive Homomorphism for Encrypted Image

Data hiding in encrypted image is a recent popular topic of data security. In this paper, we propose a reversible data hiding algorithm with pixel prediction and additive homomorphism for encrypted image. Specifically, the proposed algorithm applies pixel prediction to the input image for generating a cover image for data embedding, referred to as the preprocessed image. The preprocessed image is then encrypted by additive homomorphism. Secret data is finally embedded into the encrypted image via modular 256 addition. During secret data extraction and image recovery, addition homomorphism and pixel prediction are jointly used. Experimental results demonstrate that the proposed algorithm can accurately recover original image and reach high embedding capacity and good visual quality. Comparisons show that the proposed algorithm outperforms some recent algorithms in embedding capacity and visual quality.

A privacy-preserving parallel and homomorphic encryption scheme

In order to protect data privacy whilst allowing efficient access to data in multi-nodes cloud environments, a parallel homomorphic encryption (PHE) scheme is proposed based on the additive homomorphism of the Paillier encryption algorithm. In this paper we propose a PHE algorithm, in which plaintext is divided into several blocks and blocks are encrypted with a parallel mode. Experiment results demonstrate that the encryption algorithm can reach a speed-up ratio at about 7.1 in the MapReduce environment with 16 cores and 4 nodes.

A Randomized Response Protocol Based on Homomorphic Encryption

The investigation which involves the respondents privacy is hard to ensure the information security. To rectify this problem, Based on Homomorphic Response Protocol (RRPBH), a novel protocol using homomorphic encrypted system is proposed in this paper. The RRPBH uses the Paillier encryption algorithm to encrypt respondents answer. Paillier algorithm has the good properties of additive homomorphism and mixed multiplicative homomorphism. We can effectively compute the sum of respondents answer with better privacy protection by this protocol.