Themselves against such attacks while being compliant with regulations. Unlearning and Amnesiac Unlearning, that enable model owners to protect We follow that up with two data removal methods, namely Threat model that shows that simply removing training data is insufficient to That they may not be vulnerable to model inversion and membership inferenceĪttacks while maintaining model efficacy. ![]() Model owner or data holder may delete personal data from models in such a way Paper, we present two efficient methods that address this question of how a User's rights and their models may not be compliant with the GDPR law. Removed, then it implies that the model owner has not properly protected their Party can mount an attack and learn private information that was meant to be Information from a trained model and membership inference attacks whichĭetermine the presence of an example in a model's training data. Information leaking attacks such as model inversion attacks which extract class Unfortunately, Deep Neural Network models are vulnerable to ![]() Of their personal data, including training records used to train machine It gives EU residents the ability to request deletion Protection Regulation (GDPR) law that affects any data holder that has data onĮuropean Union residents. Download a PDF of the paper titled Amnesiac Machine Learning, by Laura Graves and 2 other authors Download PDF Abstract: The Right to be Forgotten is part of the recently enacted General Data
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