opendp 0.14.2-dev.20260401.2

A library of differential privacy algorithms for the statistical analysis of sensitive private data.
@article{Dong2019OptimalDP,
  author     = {Jinshuo Dong and
                David Durfee and
                Ryan Rogers},
  title      = {Optimal Differential Privacy Composition for Exponential Mechanisms
                and the Cost of Adaptivity},
  journal    = {CoRR},
  volume     = {abs/1909.13830},
  year       = {2019},
  url        = {http://arxiv.org/abs/1909.13830},
  eprinttype = {arXiv},
  eprint     = {1909.13830},
  timestamp  = {Mon, 02 Mar 2020 11:27:19 +0100},
  biburl     = {https://dblp.org/rec/journals/corr/abs-1909-13830.bib},
  bibsource  = {dblp computer science bibliography, https://dblp.org}
}

@misc{mckenna2020permute,
  title         = {Permute-and-Flip: A new mechanism for differentially private selection},
  author        = {Ryan McKenna and Daniel Sheldon},
  year          = {2020},
  eprint        = {2010.12603},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CR},
  url           = {https://arxiv.org/abs/2010.12603}
}

@misc{cesar2020bounding,
  title         = {Bounding, Concentrating, and Truncating: Unifying Privacy Loss Composition for Data Analytics},
  author        = {Mark Cesar and Ryan Rogers},
  year          = {2020},
  eprint        = {2004.07223},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CR},
  url           = {https://arxiv.org/abs/2004.07223}
}