Differential privacy in metric spaces: Numerical, categorical and functional data under the one roof

@article{Holohan2015DifferentialPI,
  title={Differential privacy in metric spaces: Numerical, categorical and functional data under the one roof},
  author={N. Holohan and D. Leith and O. Mason},
  journal={ArXiv},
  year={2015},
  volume={abs/1402.6124}
}
We study differential privacy in the abstract setting of probability on metric spaces. Numerical, categorical and functional data can be handled in a uniform manner in this setting. We demonstrate how mechanisms based on data sanitisation and those that rely on adding noise to query responses fit within this framework. We prove that once the sanitisation is differentially private, then so is the query response for any query. We show how to construct sanitisations for high-dimensional databases… Expand
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