Noise for differential privacy is often generated to obfuscate raw statistical results to hide identifies of specific individuals in data. Traditional methods are often inaccurate and/or computationally inefficient. In this work, a software library or program uses a differential privacy noise generator to generate high quality random numbers from the geometric distribution to both accurately and efficiently generate noise for differential privacy.
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Krueger, Brett, "A Method to Efficiently Generate Noise for Differential Privacy", Technical Disclosure Commons, (April 07, 2020)