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Implementation of calibration bounds for differential privacy in the shuffle model

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Bounds on Differential Privacy Amplification by Shuffling

Python implementation of the privacy amplification by shuffling results proved in the paper:

B. Balle, J. Bell, A. Gascon, and K. Nissim. The Privacy Blanket of the Shuffle Model, International Cryptology Conference (CRYPTO), 2019

Please include a citation to the paper if you use this code.

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