A Privacy Preserving Service-Oriented Approach for Data Anonymization Through Deep Learning

In the realm of safeguarding data privacy, particularly when it comes to sensitive personal information acquired via sensors, anonymization plays a crucial role. Within this paper, we present a novel architecture that operates on a service-oriented basis, enabling the real-time anonymization of such data. Our primary objective is to ensure that authorized users can access the data while upholding privacy standards. To achieve this, our architecture involves annotating the data upon ingestion, assigning privacy levels to groups of columns. The anonymization process relies on an autoencoder model which returns a lower-dimensional encoding of the original data. In our architecture, pretrained models are fetched from a library and updated according to a set of policies, or a new model is trained if the user requests a new set of columns. Our solution can manage any situation where anonymization policies must be managed on the fly, and provides a flexible approach to balancing privacy protection and data access. The proposed architecture can be a valuable tool for data infrastructures providers, researchers, and other organizations dealing with sensitive personal information.