Integrating BiLSTM-CRF and DOKS for Enhanced AI-Based Data Encryption in Cloud-Edge Environments
Abstract
This study addresses the challenge of secure and efficient data protection in cloud-edge collaborative environments by proposing an artificial intelligence–driven encryption framework. The method integrates a bidirectional long short-term memory network with a conditional random field model to extract sensitive information, and applies a stream encryption mechanism based on a modified Feistel structure to selectively encrypt identified entities. Experimental results show that the model achieves an accuracy of 0.99 and an F1 score of 0.94 in sensitive entity recognition. It requires only 134 milliseconds to encrypt and 183 milliseconds to decrypt one hundred kilobytes of data, and reaches ninety percent accuracy within twenty-eight training iterations. The encryption system also demonstrates strong statistical security with an average bit change rate of 45.5 percent and information entropy close to the ideal value of eight. These findings confirm that the proposed method effectively balances data confidentiality, processing efficiency, and real-time deployability for edge computing scenarios.
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DOI: https://doi.org/10.31449/inf.v49i26.10344
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