Concentrated Differential Privacy

@article{Dwork2016ConcentratedDP,
  title={Concentrated Differential Privacy},
  author={Cynthia Dwork and Guy N. Rothblum},
  journal={CoRR},
  year={2016},
  volume={abs/1603.01887}
}
The Fundamental Law of Information Recovery states, informally, that “overly accurate” estimates of “too many” statistics completely destroys privacy ([DN03] et sequelae). Differential privacy is a mathematically rigorous definition of privacy tailored to analysis of large datasets and equipped with a formal measure of privacy loss [DMNS06, Dwo06]. Moreover, differentially private algorithms take as input a parameter, typically called ε, that caps the permitted privacy loss in any execution of… CONTINUE READING
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