Aiming at the problem that it is difficult to flexibly realize, the sharing and efficient search of encrypted data in large data-bases, this paper proposes a deterministic threshold proxy re-encryption scheme under the auxiliary input model. This scheme uses Shamir's secret sharing technology to achieve threshold control, uses homomorphic signature technology to verify the legitimacy of ciphertext, and applies deterministic algorithms to solve the search problem in large databases, while ensuring the user's control over their own data, and proves its security can reach indistinguishable semantic security (PRIV1-INDr) under the standard model. Compared with other schemes, this scheme not only shortens the length of the ciphertext and improves the decryption efficiency, but it also has anti-auxiliary input, robustness, and multi-hop characteristics and can better meet actual needs.

In the era of Industry 4.0, the Industrial Internet of Things (IIoT) has been applied to help physical entities to access real-time data in the network and share critical information. However, both the decentralization of IIoT and the heterogeneity of different devices constituting IIoT also pose a serious threat to secure communication between entities. Although the encryption technologies can ensure the confidentiality of communication data, the security of key becomes more important for devices in IIoT. Recently, some asymmetric group key agreement (AGKA) protocols have been proposed to negotiate a common encryption key for group members, and each group member holds its decryption key. However, the existing AGKA protocols fail to effectively control the access of agreement participants in IIoT. To solve this problem, in this article, we propose an AGKA protocol based on blockchain and attribute for IIoT. The proposed protocol not only realizes the access control of agreement participants but also realizes the automation of access control, the tamper resistance, and the nonrepudiation of the agreement process. The security and performance analysis of the proposed protocol show that it is more secure and effective compared with the existing AGKA protocols.

Most of the traditional 2D image hashing schemes do not take into account the change of viewpoint when constructing the final hash vector. This result in the classification accuracy rate is unsatisfactory when applied for depth-image-based rendering (DBIR) 3D image identification. In this work, pixel grouping based on histogram shape and nonnegative matrix factorization (NMF) are applied to design DIBR 3D image hashing with better robustness resisting to geometric distortions and higher classification accuracy rate for virtual image identification. Experiments show that the proposed hashing is robust against common signal and geometric distortion attacks, such as additive noise, blurring, JPEG compression, scaling, and rotation. Compared with the state-of-art schemes of traditional 2D image hashing, the proposed hashing achieves better performances under above attacks, especially for virtual image identification.