Data Minimization for GDPR Compliance in Machine Learning Models

• URL: http://arxiv.org/abs/2008.04113v1
• Date: Thu, 6 Aug 2020 08:21:15 GMT
• Title: Data Minimization for GDPR Compliance in Machine Learning Models
• Authors: Abigail Goldsteen, Gilad Ezov, Ron Shmelkin, Micha Moffie, Ariel Farkash
• Abstract summary: EU General Data Protection Regulation requires only data necessary to fulfill a certain purpose to be collected. We present a first-of-a-kind method to reduce the amount of personal data needed to perform predictions. Our method makes use of knowledge encoded within the model to produce a generalization that has little impact on its accuracy.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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