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Differentially-private data synthetisation for efficient re-identification risk control
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DOI：10.1007/s10994-025-06799-w.png)
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Abstract
En 中文
Protecting user data privacy can be achieved via many methods, from statistical transformations to generative models. However, they all have critical drawbacks. For example, creating a transformed data set using traditional techniques is highly time-consuming. Also, recent deep learning-based solutions require significant computational resources in addition to long training phases, and differentially private-based solutions may undermine data utility. In this paper, we propose $$\epsilon$$ -PrivateSMOTE, a technique designed to protect against re-identification and linkage attacks, particularly addressing cases with a high re-identification risk. Our proposal combines synthetic data generation via noise-induced interpolation with differential privacy principles to obfuscate high-risk cases. We demonstrate how $$\epsilon$$ -PrivateSMOTE is capable of achieving competitive results in privacy risk and better predictive performance when compared to multiple traditional and state-of-the-art privacy-preservation methods, including generative adversarial networks, variational autoencoders, and differential privacy baselines. We also show how our method improves time requirements by at least a factor of 9 and is a resource-efficient solution that ensures high performance without specialised hardware.
Keywords:
Data privacy
Synthetic data
Deep learning
Differential privacy
Resource-efficiency
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