Oct 18, 2021 · Our main contribution is in augmenting differential privacy with {\em Flexible Accuracy}, which allows small distortions in the input (e.g., ...
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Our main contribution is in augmenting differential privacy with Flexible Accuracy, which allows small distortions in the input. (e.g., dropping outliers) ...
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We give our new composition theorems for flexible accuracy and robust privacy, along-with a new composition theorem for differential privacy via pre-processing ...
The smaller the value is, the better privacy protection. Accuracy: The closeness of the output of DP algorithms to the pure output. In the Private Machine ...
The cost of differential privacy is a reduction in the model's accuracy. We demonstrate that in the neural networks trained using differentially private.
Missing: Flexible | Show results with:Flexible
Aug 31, 2020 · Not only does it protect against clever adversaries, it gives us a lot of flexibility in designing differentially private mechanisms: once ...
Oct 18, 2021 · Differential Privacy (DP) has become a gold standard in privacy-preserving data analysis. While it provides one of the most rigorous notions ...
Jul 19, 2024 · Compared to the leading prior work, DP-CERT gives up to a 2.5 × \times × increase in certified accuracy for the same differential privacy ...
The main contribution is in augmenting differential privacy with Em Flexible Accuracy, which allows small distortions in the input before measuring accuracy ...
Differential privacy is a rather new privacy technique that maximizes the accuracy of queries from statistical databases while minimizing the chances of ...