Biometrics, with its uniqueness to every individual, has been adapted as a security authentication feature by many institutions. These biometric data are processed into templates that are saved on databases, and a central authority centralizes and controls these databases. This form of storing biometric data, or in our case fingerprint template, is asymmetric and prone to three main security attacks, such as fake template input, template modification or deletion, and channel interception by a malicious attacker. In this paper, we secure an encrypted fingerprint template by a symmetric peer-to-peer network and symmetric encryption. The fingerprint is encrypted by the symmetric key algorithm: Advanced Encryption Standard (AES) algorithm and then is uploaded to a symmetrically distributed storage system, the InterPlanetary File system (IPFS). The hash of the templated is stored in a decentralized blockchain. The slow transaction speed of the blockchain has limited its use in real-life applications, such as large file storage, hence, the merge with IPFS to store just the hashes of large files. The encrypted template is uploaded to the IPFS, and its returned digest is stored on the Ethereum network. The implementation of IPFS prevents storing the raw state of the fingerprint template on the Ethereum network in order to reduce cost and also prevent identity theft. This procedure is an improvement of previous systems. By adopting the method of template hashing, the proposed system is cost-effective and efficient. The experimental results depict that the proposed system secures the fingerprint template by encryption, hashing, and decentralization.

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.