A Linear Reconstruction Approach for Attribute Inference Attacks against Synthetic Data. (arXiv:2301.10053v1 [cs.LG])

Web: http://arxiv.org/abs/2301.10053

Personal data collected at scale from surveys or digital devices offers
important insights for statistical analysis and scientific research. Safely
sharing such data while protecting privacy is however challenging.
Anonymization allows data to be shared while minimizing privacy risks, but
traditional anonymization techniques have been repeatedly shown to provide
limited protection against re-identification attacks in practice. Among modern
anonymization techniques, synthetic data generation (SDG) has emerged as a
potential solution to find a good tradeoff between privacy and statistical
utility. …

attacks data synthetic data