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Differential Privacy Made Easy | | Muhammad Aitsam
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1 Jan 2022 | | Abstract: | Data privacy is a major issue for many decades, several techniques have been
developed to make sure individuals’ privacy but still world has seen privacy
failures. In 2006, Cynthia Dwork gave the idea of Differential Privacy which
gave strong theoretical guarantees for data privacy. Many companies and
research institutes developed differential privacy libraries, but in order to
get the differentially private results, users have to tune the privacy
parameters. In this paper, we minimized these tune-able parameters. The
DP-framework is developed which compares the differentially private results of
three Python based DP libraries. We also introduced a new very simple DP
library (GRAM-DP), so the people with no background of differential privacy can
still secure the privacy of the individuals in the dataset while releasing
statistical results in public. | | Source: | arXiv, 2201.00099 | | Services: | Forum | Review | PDF | Favorites | |
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