Linear and Range Counting under Metric-based Local Differential Privacy

@article{Xiang2020LinearAR,
  title={Linear and Range Counting under Metric-based Local Differential Privacy},
  author={Zhuolun Xiang and B. Ding and X. He and Jingren Zhou},
  journal={2020 IEEE International Symposium on Information Theory (ISIT)},
  year={2020},
  pages={908-913}
}
  • Zhuolun Xiang, B. Ding, +1 author Jingren Zhou
  • Published 2020
  • Computer Science
  • 2020 IEEE International Symposium on Information Theory (ISIT)
  • Local differential privacy (LDP) enables private data sharing and analytics without the need for a trusted data collector. Error-optimal primitives (for, e.g., estimating means and item frequencies) under LDP have been well studied. For analytical tasks such as range queries, however, the best known error bound is dependent on the domain size of private data, which is potentially prohibitive. This deficiency is inherent as LDP protects the same level of indistinguishability between any pair of… CONTINUE READING
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