A hybrid anonymization pipeline to improve the privacy-utility balance in sensitive datasets for ML purposes
Jenno Verdonck, Kevin De Boeck, Michiel Willocx, Jorn Lapon, Vincent Naessens · 2023
The modern world is data-driven. Businesses increasingly take strategic decisions based on customer data, and companies are founded with a sole focus of performing machine-learning driven data analytics for third parties. External data sources containing sensitive records are often required to build qualitative machine learning models and, hence, perform accurate and meaningful predictions. However, exchanging sensitive datasets is no sinecure. Personal data must be managed according to privacy regulation. Similarly, loss of strategic data can negatively impact the competitiveness of a company. In both cases, dataset anonymization can overcome the aforementioned obstacles.