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.

Anonymizing Machine Learning Models

There is a known tension between the need to analyze personal data to drive business and privacy concerns. Many data protection regulations, including the EU General Data Protection Regulation (GDPR) and the California Consumer Protection Act (CCPA), set out strict restrictions and obligations on companies that collect or process personal data. Moreover, machine learning models themselves can be used to derive personal information, as demonstrated by recent membership and attribute inference attacks. Anonymized data, however, is exempt from data protection principles and obligations. Thus, models built on anonymized data are also exempt from any privacy obligations, in addition to providing better protection against such attacks on the training data. Learning on anonymized data typically results in a significant degradation in accuracy. We address this challenge by guiding our anonymization using the knowledge encoded within the model, and targeting it to minimize the impact on the model's accuracy, a process we call accuracy-guided anonymization. We demonstrate that by focusing on the model's accuracy rather than information loss, our method outperforms state of the art k-anonymity methods in terms of the achieved utility, in particular with high values of k and large numbers of quasi-identifiers. We also demonstrate that our approach achieves similar results in its ability to prevent membership inference attacks as alternative approaches based on differential privacy. This shows that model-guided anonymization can, in some cases, be a legitimate substitute for such methods, while averting some of their inherent drawbacks such as complexity, performance overhead and being fitted to specific model types. As opposed to methods that rely on adding noise during training, our approach does not rely on making any modifications to the training algorithm itself.