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How to Make Your Approximation Algorithm Private: A Black-Box
  Differentially-Private Transformation for Tunable Approximation Algorithms of
  Functions with Low Sensitivity

How to Make Your Approximation Algorithm Private: A Black-Box Differentially-Private Transformation for Tunable Approximation Algorithms of Functions with Low Sensitivity

7 October 2022
Jeremiah Blocki
Elena Grigorescu
Tamalika Mukherjee
Samson Zhou
ArXivPDFHTML

Papers citing "How to Make Your Approximation Algorithm Private: A Black-Box Differentially-Private Transformation for Tunable Approximation Algorithms of Functions with Low Sensitivity"

2 / 2 papers shown
Title
Counting Distinct Elements in the Turnstile Model with Differential
  Privacy under Continual Observation
Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation
Palak Jain
Iden Kalemaj
Sofya Raskhodnikova
Satchit Sivakumar
Adam D. Smith
37
11
0
11 Jun 2023
Better Differentially Private Approximate Histograms and Heavy Hitters using the Misra-Gries Sketch
Better Differentially Private Approximate Histograms and Heavy Hitters using the Misra-Gries Sketch
C. Lebeda
Jakub Tvetek
26
1
0
06 Jan 2023
1