We introduce Concentrated Differential Privacy, a relaxation of Differential Privacy enjoying better accuracy than both pure differential privacy and its popular "(epsilon,delta)" relaxation without compromising on cumulative privacy loss over multiple computations.
No takes yet. Share an insight, caveat, or question.
Dwork et al. (2016) studied this question.
Synapse has enriched one closely related paper. Consider it for comparative context: