Covariance's Loss is Privacy's Gain: Computationally Efficient, Private and Accurate Synthetic Data. (arXiv:2107.05824v2 [cs.CR] UPDATED)

The protection of private information is of vital importance in data-driven
research, business, and government. The conflict between privacy and utility
has triggered intensive research in the computer science and statistics
communities, who have developed a variety of methods for privacy-preserving
data release. Among the main concepts that have emerged are anonymity and
differential privacy. Today, another solution is gaining traction, synthetic
data. However, the road to privacy is paved with NP-hard problems. In this
paper we focus on the …

data loss privacy synthetic data