Workshop on Privacy in NLP (PrivateNLP 2020)
Oluwaseyi Feyisetan, Sepideh Ghanavati, Patricia Thaine · 2020
Privacy-preserving data analysis has become essential in Machine Learning (ML), where access to vast amounts of data can provide large gains the in accuracies of tuned models. A large proportion of user-contributed data comes from natural language e.g., text transcriptions from voice assistants. It is therefore important for curated natural language datasets to preserve the privacy of the users whose data is collected and for the models trained on sensitive data to only retain non-identifying (i.e., generalizable) information. The workshop aims to bring together researchers and practitioners from academia and industry to discuss the challenges and approaches to designing, building, verifying, and testing privacy-preserving systems in the context of Natural Language Processing (NLP).