Traditionally, unified data embedding and scrambling techniques have been designed for grayscale images, which cannot be applied directly to a three-dimensional (3D) mesh. Recently, the universal use of 3D technology inspired us to innovate in this field. In this paper, an adaptive unified data embedding and scrambling technique for 3D mesh models (3D-AUES) is proposed, which can embed external data and scramble 3D mesh simultaneously. First, a vertex coordinate prediction method called cross prediction is adopted to accurately predict half of the vertices from the other half. The predicted vertices are used to embed external data. We further increase the embedding rate by bit replacement embedding. Then, to improve security, we propose an adaptive threshold to select vertices for embedding. To ensure lossless scrambling, the thresholds and prediction errors are embedded as side information with secret information into the vertices. By adopting an adaptive threshold and multilayer embedding, scalable scrambling quality can be achieved. On the decoder side, with the help of losslessly embedded side information, external data can be successfully extracted, and the original mesh can be restored to predetermined distortion levels, from lossless recovery to partial recovery. Experiments show that 3D-AUES has a high embedding rate, scalable scrambling quality and scalable recovery quality. INDEX TERMS Adaptive threshold, bit replacement, reversible data hiding, three-dimensional mesh models, unified embedding-scrambling.

The patient's medical health record (PMHR) has always provided a large amount of research data to medical institutions and pharmaceutical companies, etc., and has contributed to the development in medical research. However, such PMHR data contains the patient's personal privacy and should be shared under the control of the patients, not the hospital where this data is acquired. In order to protect the privacy of PMHR data while realizing efficient data sharing, this paper proposes a blockchain-based sharing and protection scheme. In this solution, the PMHR data are encrypted and stored in a cloud server, which is equipped with an access control scheme implemented as a smart contract on a blockchain. Different from previous works, in order to ensure efficient access and reduce the workload of patients, the types of users who can apply for access are limited to hospitals and pharmaceutical companies. In order to resist the potential Man-in-the-middle (MITM) attack, we have introduced an improved proxy re-encryption scheme to ensure the secrecy of PMHR data while reducing the computational complexity. The whole system is implemented using Solidity and tested on 10 nodes for function verification. Experimental result shows that the proposed system is more efficient than previous systems. Security under the MITM attack is also ensured by security analysis.