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Efficient Privacy-Preserving Synthetic Data Generation for Tabular Data Sharing
ϵ-PrivateSMOTE is a novel strategy that combines synthetic data generation via noise-induced interpolation with Differential Privacy principles to efficiently safeguard against re-identification and linkage attacks, particularly for high-risk cases.
Knowledge Infusion in Privacy-Preserving Synthetic Data Generation: The KIPPS Framework
The KIPPS framework enhances privacy-preserving synthetic data generation by incorporating domain knowledge from Knowledge Graphs to address challenges related to data diversity, complexity, and domain-specific constraints.
