Invoice data is critical and highly sensitive for enterprises, yet it stands as one of the most extensively used data categories in the financial credit sector. Participants in the credit market, engaged in corporate lending activities, face the dual challenge of ensuring the security of enterprise invoice data and safeguarding the integrity of institutional rules. Traditional data computation and encryption present a dilemma, as two encrypted datasets cannot directly engage in calculations, and two datasets participating in calculations must be in plaintext. This necessitates the convergence of enterprise data and institutional rules for computation. Consequently, two sets of data must be exposed to one of the parties or a mutually trusted third party. In practice, identifying a genuinely trusted third party proves to be a formidable task. This paper introduces a federated privacy computing method based on an oblivious transfer algorithm in the context of risk control admission rules within the credit risk management field. This approach enables both parties holding data to determine admission rules without disclosing their own data.
Cow face identification plays a crucial role in the cattle management system. Previous studies have primarily focused on radio frequency identification, and a few researchers devote to the cow face identification field. In this paper, instead of solely extracting features from individual images, we have constructed datasets for cow face identification. The datasets include the facial images of an all-black cow, an all-white cow, and a mixed black-and-white cow. We apply the convolutional neural networks method by utilizing ResNet backbone architectures, and additionally, we incorporate different loss functions and attention modules to enhance the model’s capacity. The results demonstrate that our methods have achieved an identification accuracy rate of 97.04% and FRR of 5.06%, which also improves identification speed and performance compared to other studies, marking a notable advancement in cow face identification.
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