Differentially-Private Data Synthetisation for Efficient Re-Identification Risk Control

arXiv:2212.00484v3 Announce Type: replace-cross
Abstract: 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, …

addition arxiv can computational consuming control critical cs.cr cs.lg data data privacy deep learning generative generative models identification privacy private private data protecting re-identification resources risk solutions techniques user data