Titlebook: Data and Applications Security and Privacy XXXV; 35th Annual IFIP WG Ken Barker,Kambiz Ghazinour Conference proceedings 2021 IFIP Internat

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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.

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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.

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