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On the Lift, Related Privacy Measures, and Applications to Privacy-Utility Trade-Offs.

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This study introduces asymmetric local information privacy (ALIP) to address lift asymmetry in datasets. ALIP enhances data utility by applying different privacy bounds to maximum and minimum lifts, improving on existing methods.

Keywords:
local differential privacylocal information privacyoptimal random responsewatchdog privacy mechanism

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Area of Science:

  • Computer Science
  • Data Privacy
  • Machine Learning

Background:

  • Lift quantifies knowledge gain about sensitive data features.
  • Existing privacy methods bound maximum and minimum lifts symmetrically.
  • Lift asymmetry, the distinct ranges of max- and min-lifts, is not adequately addressed.

Purpose of the Study:

  • To introduce Asymmetric Local Information Privacy (ALIP) to handle lift asymmetry.
  • To improve data utility in privacy mechanisms like watchdog and optimal random response (ORR).
  • To propose novel lift-inverse measures to address min-lift leakage.

Main Methods:

  • Proposed Asymmetric Local Information Privacy (ALIP) with differential bounds for min- and max-lifts.
  • Integrated ALIP into watchdog and optimal random response (ORR) mechanisms.
  • Introduced subset merging for watchdog and subset random response for ORR.
  • Investigated lift-based measures (ℓ1-norm, χ2, α-lift) and proposed lift-inverse measures.

Main Results:

  • ALIP enhances data utility compared to symmetric local information privacy.
  • Subset merging and random response improve utility and reduce complexity in privacy mechanisms.
  • Existing lift-based measures inadequately protect against min-lift leakage.
  • Lift-inverse measures effectively restrict min-lift, complementing existing methods.

Conclusions:

  • ALIP offers a more effective approach to privacy by acknowledging lift asymmetry.
  • Lift-based and lift-inverse measures provide tunable privacy-utility trade-offs, with ALIP as a robust framework.
  • The proposed methods enhance data utility while maintaining robust privacy guarantees against knowledge gain.