SDkA: Synthetic Data Integrated k-Anonymity Model for Data Sharing With Improved Utility

Summary

This paper proposes SDkA, an anonymity model that combines synthetic data with k-anonymity for data sharing. Synthetic data from a conditional generative adversarial network, together with an automated quality-fixing method, addresses size, quantity and diversity issues. Selective generalization is added to the k-anonymity model to avoid unnecessary generalization, and the authors report that SDkA defends against diverse attacks and improves utility over standard k-anonymity.

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