Hybrid QML models trained with classical DP-SGD retain higher accuracy than classical models under fixed privacy budgets on synthetic and image-classification tasks.
URL https: //ojs.aaai.org/index.php/AAAI/article/view/17123
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Differential privacy reduces algorithmic collective action effectiveness, with formal lower bounds on success probability depending on collective size and privacy parameters, plus experimental verification on neural nets.
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Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy
Differential privacy reduces algorithmic collective action effectiveness, with formal lower bounds on success probability depending on collective size and privacy parameters, plus experimental verification on neural nets.