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Averaging Attacks on Bounded Noise-based Disclosure Control Algorithms

2020
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Proceedings on Privacy Enhancing Technologies
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AbstractWe describe and evaluate an attack that reconstructs the histogram of any target attribute of a sensitive dataset which can only be queried through a specific class of real-world privacy-preserving algorithms which we call bounded perturbation algorithms. A defining property of such an algorithm is that it perturbs answers to the queries by adding zero-mean noise distributed within a bounded (possibly undisclosed) range. Other key properties of the algorithm include only allowing

doi:10.2478/popets-2020-0031
fatcat:nyle7jnirzehbhod35dgg2gw3a