How to Make Your Approximation Algorithm Private: A Black-Box Differentially-Private Transformation for Tunable Approximation Algorithms of Functions with Low Sensitivity.
Jeremiah BlockiElena GrigorescuTamalika MukherjeeSamson ZhouPublished in: CoRR (2022)
Keyphrases
- approximation algorithms
- approximation ratio
- black box
- np hard
- worst case
- polynomial time approximation
- vertex cover
- constant factor
- special case
- constant factor approximation
- approximation guarantees
- set cover
- primal dual
- computational complexity
- optimal solution
- learning algorithm
- open shop
- undirected graph
- differentially private
- error bounds
- linear programming
- randomized algorithms
- error rate
- upper bound
- databases
- data sets