  Deep learning models are nowadays broadly deployed to solve an incredibly
large variety of tasks. However, little attention has been devoted to connected
legal aspects. In 2016, the European Union approved the General Data Protection
Regulation which entered into force in 2018. Its main rationale was to protect
the privacy and data protection of its citizens by the way of operating of the
so-called "Data Economy". As data is the fuel of modern Artificial
Intelligence, it is argued that the GDPR can be partly applicable to a series
of algorithmic decision making tasks before a more structured AI Regulation
enters into force. In the meantime, AI should not allow undesired information
leakage deviating from the purpose for which is created. In this work we
propose DisP, an approach for deep learning models disentangling the
information related to some classes we desire to keep private, from the data
processed by AI. In particular, DisP is a regularization strategy
de-correlating the features belonging to the same private class at training
time, hiding the information of private classes membership. Our experiments on
state-of-the-art deep learning models show the effectiveness of DisP,
minimizing the risk of extraction for the classes we desire to keep private.
