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# anonymization module
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This module contains methods for anonymizing ML model training data, so that when
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a model is retrained on the anonymized data, the model itself will also be considered
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anonymous. This may help exempt the model from different obligations and restrictions
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set out in data protection regulations such as GDPR, CCPA, etc.
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The module contains methods that enable anonymizing training datasets in a manner that
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is tailored to and guided by an existing, trained ML model. It uses the existing model's
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predictions on the training data to train a second, anonymizer model, that eventually determines
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the generalizations that will be applied to the training data. For more information about the
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method see: https://arxiv.org/abs/2007.13086
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Once the anonymized training data is returned, it can be used to retrain the model.
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The following figure depicts the overall process:
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<p align="center">
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<img src="../../docs/images/AI_Privacy_project2.jpg?raw=true" width="667" title="anonymization process">
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</p>
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<br />
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2022-02-01 12:27:22 +02:00
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Citation
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--------
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Goldsteen A., Ezov G., Shmelkin R., Moffie M., Farkash A. (2022) Anonymizing Machine Learning Models. In: Garcia-Alfaro
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J., Muñoz-Tapia J.L., Navarro-Arribas G., Soriano M. (eds) Data Privacy Management, Cryptocurrencies and Blockchain
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Technology. DPM 2021, CBT 2021. Lecture Notes in Computer Science, vol 13140. Springer, Cham.
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https://doi.org/10.1007/978-3-030-93944-1_8
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2021-04-28 14:00:19 +03:00
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