K-anonymity privacy-preserving data sharing for a dynamic game scheme
Aiming for fact that the deep trained learning model has some problems,such as lack of a large amount of labeled training data and data privacy leakage,a k-anonymity privacy-preserving data sharing for the dynamic game(KPDSDG)scheme is proposed.First,by using the dynamic game strategy,the optimal data k-anonymization scheme is designed,which achieves secure data sharing while protecting data privacy.Second,a data anonymization evaluation framework is proposed to evaluate data anonymization schemes based on the availability,privacy,and information loss of anonymous data,which can further improve the privacy and availability of data and reduce the risk of reidentification.Finally,owing to adopting the conditional generative adversarial network to generate data,the problem that model training lacks a large amount of labeled training samples is solved.The security analysis shows that the entire sharing process can ensure that the privacy information of the data owner is not leaked.Meanwhile,experiment shows that the accuracy of the model trained on the data generated after privacy in this scheme is higher than that of other schemes,with the optimal situation being 8.83% higher,that the accuracy of the proposed solution in this paper is basically consistent with the accuracy of the model trained based on raw data,with a difference of only 0.34% in the optimal situation and that the scheme has a lower computing cost.Therefore,the scheme satisfies data anonymity,data augmentation,and data security sharing simultaneously.
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