Athena: Probabilistic Verification of Machine Unlearning
نویسندگان
چکیده
The right to be forgotten, also known as the erasure, is of individuals have their data erased from an entity storing it. status this long held notion was legally solidified recently by General Data Protection Regulation (GDPR) in European Union. As a consequence, there need for mechanisms whereby users can verify if service providers comply with deletion requests. In work, we take first step proposing formal framework, called Athena, study design such verification requests – machine unlearning context systems that provide learning (MLaaS). Athena allows rigorous quantification any mechanism based on hypothesis testing. Furthermore, propose novel leverages backdoors and demonstrate its effectiveness certifying high confidence, thus providing basis quantitatively inferring unlearning. We evaluate our approach over range network architectures multi-layer perceptrons (MLP), convolutional neural networks (CNN), residual (ResNet), short-term memory (LSTM) 6 different datasets. that: (1) has minimal effect accuracy ML but provides confidence unlearning, even multiple employ system ascertain compliance requests, (2) robust against servers deploying state-of-the-art backdoor defense methods. Overall, foundation quantitative analysis verifying which support legal regulatory frameworks pertaining users’
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ژورنال
عنوان ژورنال: Proceedings on Privacy Enhancing Technologies
سال: 2022
ISSN: ['2299-0984']
DOI: https://doi.org/10.56553/popets-2022-0072