1 research outputs found
Data Minimization for GDPR Compliance in Machine Learning Models
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