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Flexible Accuracy for Differential Privacy
[article]
2021
arXiv
pre-print
Differential Privacy (DP) has become a gold standard in privacy-preserving data analysis. While it provides one of the most rigorous notions of privacy, there are many settings where its applicability is limited. Our main contribution is in augmenting differential privacy with Flexible Accuracy, which allows small distortions in the input (e.g., dropping outliers) before measuring accuracy of the output, allowing one to extend DP mechanisms to high-sensitivity functions. We present mechanisms
arXiv:2110.09580v1
fatcat:6hrekblz5bg2bfutupbwp5gcmi