Robust Local Differential Privacy

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We consider data release protocols for data X = (S, U), where S is sensitive; the released data Y contains as much information about X as possible, measured as I(X; Y ), without leaking too much about S. We introduce the Robust Local Differential Privacy (RLDP) framework to measure privacy. This framework relies on the underlying distribution of the data, which needs to be estimated from available data. Robust privacy guarantees ensure privacy for all distributions in a confidence set based on this estimate. We also present three algorithms that construct RLDP protocols from a given dataset. One of these approximates the confidence set by a polytope and uses results from robust optimisation to yield high utility release protocols. However, it relies on vertex enumeration and becomes computationally infeasible for large input alphabets. The other two algorithms are low-complexity and build on randomised response. Experiments verify that all three algorithms offer significantly improved utility over regular LDP.
Original languageEnglish
Title of host publicationIEEE International Symposium on Information Theory
Subtitle of host publicationISIT
Number of pages6
ISBN (Electronic)978-1-5386-8209-8
ISBN (Print)978-1-5386-8210-4
Publication statusPublished - 1 Sept 2021
EventIEEE International Symposium on Information Theory, ISIT 2021 - Virtual Event, Melbourne, Australia
Duration: 12 Jul 202120 Jul 2021


ConferenceIEEE International Symposium on Information Theory, ISIT 2021
Abbreviated titleISIT 2021
Internet address


  • Differential privacy
  • Privacy
  • Uncertainty
  • Protocols
  • Closed-form solutions
  • Publishing
  • Robustness


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