The widespread use of cloud computing applications has created a society-wide debate on how user privacy is handled by online service providers. Regulations such as the European Union's General Data Protection Regulation (GDPR), have put forward restrictions on how such services are allowed to handle user data. The field of privacy-preserving machine learning is a response to this issue that aims to develop secure classifiers for remote prediction, where both the client's data and the server's model are kept private. This is particularly relevant in the case of speech, and concerns not only the linguistic contents, but also the paralinguistic and extralinguistic info that may be extracted from the speech signal.

In this talk we provide a brief overview of the current state-of-the-art in paralinguistic and extralinguistic tasks for a major application area in terms of privacy concerns - health, along with an introduction to cryptographic methods commonly used in privacy-preserving machine learning. These will lay the groundwork for the review of the state-of-the-art of privacy in paralinguistic and extralinguistic tasks for health applications. With this talk we hope to raise awareness to the problem of preserving privacy in this type of tasks and provide an initial background for those who aim to contribute to this topic.