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Privacy-Preserving Synthetic Location Data in the Real World
[article]
2021
arXiv
pre-print
Sharing sensitive data is vital in enabling many modern data analysis and machine learning tasks. However, current methods for data release are insufficiently accurate or granular to provide meaningful utility, and they carry a high risk of deanonymization or membership inference attacks. In this paper, we propose a differentially private synthetic data generation solution with a focus on the compelling domain of location data. We present two methods with high practical utility for generating
arXiv:2108.02089v1
fatcat:qvymbuhcobhupflob7xxjqdzti