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• W3048339652 abstract "The EU General Data Protection Regulation (GDPR) mandates the principle of data minimization, which requires that only data necessary to fulfill a certain purpose be collected. However, it can often be difficult to determine the minimal amount of data required, especially in complex machine learning models such as neural networks. We present a first-of-a-kind method to reduce the amount of personal data needed to perform predictions with a machine learning model, by removing or generalizing some of the input features. Our method makes use of the knowledge encoded within the model to produce a generalization that has little to no impact on its accuracy. This enables the creators and users of machine learning models to acheive data minimization, in a provable manner." @default.
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• W3048339652 date "2020-08-06" @default.
• W3048339652 modified "2023-09-25" @default.
• W3048339652 title "Data Minimization for GDPR Compliance in Machine Learning Models" @default.
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