Differentially-Private Data Synthetisation for Efficient Re-Identification Risk Control
arxiv(2022)
摘要
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 ϵ-PrivateSMOTE,
a technique designed for safeguarding 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 ϵ-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.
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