Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations

Understand

Accurately learning from user data while providing quantifiable privacy guarantees provides an opportunity to build better ML models while maintaining user trust.

• This paper presents a formal approach to carrying out privacy preserving text perturbation using the notion of dx-privacy designed to achieve geo-indistinguishability in location data.
• Our approach applies carefully calibrated noise to vector representation of words in a high dimension space as defined by word embedding models.
• We present a privacy proof that satisfies dx-privacy where the privacy parameter epsilon provides guarantees with respect to a distance metric defined by the word embedding space.