  Protecting user data privacy can be achieved via many methods, from
statistical transformations to generative models. However, all of them 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 for safeguarding against re-identification and linkage
attacks, particularly addressing cases with a high \sloppy 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.
