Facial recognition and resolution technology have extensive application scenarios in the era of big data. It ensures the consistency of personal identity in physical space and cyberspace by establishing correspondence between physical objects and network entities. However, massive data brings huge processing pressure to cloud service, and there are data leakage risks about personal information. To address this problem, we propose a privacy security protection scheme for facial recognition and resolution based on edge computing. Firstly, a facial recognition and resolution framework based on edge computing is established, which improves the communication and storage efficiency through task partition and relieves the pressure of cloud computing. Then, a verifiable deletion scheme based on Hidden CP-ABE is proposed to provide fine-grained access control and ensure the safe deletion of target data in the cloud. Moreover, after applying the verifiable deletion method, the safe deletion of the target data in the cloud can be achieved. Finally, the simulation results show the effectiveness and security of the proposed scheme.

Online crowdfunding, an innovative model based on “[Formula: see text]”, is a hot spot for financing via Internet. Crowdfunding based on blockchain is an emerging economic phenomenon and becomes one of the most advanced risk financing strategies. However, crowdfunding transactions face security threats due to identity leaks, quantum attacks and the untraceable nature of blind signatures, which facilitate criminal activity. Different from the previous works, which ignored the importance of traceability, in this paper, we establish a blockchain-empowered secure crowdfunding architecture and propose an anti-quantum partially blind signature algorithm based on the verifiable identity of both sides. Specially, for one thing, the private key decided by user identity is generated by lattice-based sample matrix, and the privacy of user identity can be ensured and traced by the rejection sampling theorem. For another thing, we design an improved krill herd algorithm (IKHA) to increase the credit factor of fundraisers for dealing with project investment issues. The simulation evaluates the correctness and effectiveness of our theoretical analyses. Compared with the current popular schemes, the proposed IKH algorithm has a higher convergence speed and can optimize investment efficiency.