In today's world, with over 150 countries having their own data protection laws, global data governance has become a significant challenge for enterprises. Moreover, the stakes are getting higher, with recent GDPR fines demonstrating that regulators' patience for non-compliance is dwindling.
In this article, we explore a recommended path towards responsible, compliant machine learning on regulated data, which involves applying data minimization principles and relevant privacy-enhancing technologies (PETs) to your ML systems.
Data minimization is a fundamental privacy principle found in privacy laws worldwide, including GDPR. It means that only relevant, adequate, and necessary personal data should be collected and kept for as little time as possible. Federated learning, an approach for training machine learning models in distributed settings, aligns well with GDPR's principles of data minimization. By avoiding unnecessary data duplication, federated learning enables the principle of data being limited to what is necessary and can help ensure that the data is used solely for its intended purpose.
Google is an early adopter of data minimization principles and federated learning, using it to train their ML model for next-word prediction in virtual keyboards for smartphones. In this example, Google adheres to data minimization principles by
By applying privacy-enhancing technologies to this service, which requires ML trained on personal data, Google can provide a high-value service to Android users while respecting their data privacy.
The recommended path towards responsible, compliant machine learning on regulated data involves applying data minimization principles and relevant PETs, such as federated learning. Enterprises embracing this approach can enable valuable tools and services based on data insights and machine learning on privacy-regulated data while respecting data privacy laws.
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