circumvent 发表于 2025-3-26 21:47:18
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G. Korniss,P. A. Rikvold,M. A. Novotnye, we propose new techniques to improve the outcome of differentially private learning and provide the privacy analysis of the overall solution. Through a comprehensive evaluation with large-scale network flow data, we demonstrate that our solution is capable of producing realistic network flows.Commentary 发表于 2025-3-27 16:12:05
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Not a Free Lunch, But a Cheap One: On Classifiers Performance on Anonymized Datasetsy, we perform extensive experiments to verify how the classifiers performance changes when trained on an anonymized dataset compared to the original one, and evaluate the impact of classification algorithms, datasets properties, and anonymization parameters on classifiers’ performance.休闲 发表于 2025-3-28 13:44:01
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