How Much User Context Do We Need? Privacy by Design in Mental Health NLP Application

TL;DR

This paper explores the balance between user privacy and data utility in mental health NLP, demonstrating that incorporating more user context can enhance model performance while maintaining privacy guarantees.

Contribution

It provides the first analysis of user history length versus differential privacy in mental health NLP, showing how more context can improve utility without compromising privacy.

Findings

Increasing user context improves model utility.

Differential privacy can be balanced with user data volume.

More user history can be used while preserving privacy.

Abstract

Clinical NLP tasks such as mental health assessment from text, must take social constraints into account - the performance maximization must be constrained by the utmost importance of guaranteeing privacy of user data. Consumer protection regulations, such as GDPR, generally handle privacy by restricting data availability, such as requiring to limit user data to 'what is necessary' for a given purpose. In this work, we reason that providing stricter formal privacy guarantees, while increasing the volume of user data in the model, in most cases increases benefit for all parties involved, especially for the user. We demonstrate our arguments on two existing suicide risk assessment datasets of Twitter and Reddit posts. We present the first analysis juxtaposing user history length and differential privacy budgets and elaborate how modeling additional user context enables utility preservation while maintaining acceptable user privacy guarantees.