Overview

Privacy-preserving data analysis has become essential in the age of Machine Learning (ML) where access to vast amounts of data can provide gains over tuned algorithms. A large proportion of user-contributed data comes from natural language e.g., text transcriptions from voice assistants.

It is therefore important to curate NLP datasets while preserving the privacy of the users whose data is collected, and train ML models that only retain non-identifying user data.

The workshop aims to bring together practitioners and researchers 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.

Information about the workshop's topics of interest can be found in the Call for Papers.

Key Dates

• Submission Deadline: April 8, 2022 April 20, 2022 (11.59pm UTC-12)

• Acceptance Notification: May 6, 2022

• Camera-ready versions: May 20, 2022

• Workshop: July 15, 2022

Keynote Speaker

• Ilya Mironov (Meta)

Invited Speakers

• Franziska Boenisch (Fraunhofer AISEC)

• Esha Ghosh (Microsoft)

Agenda

Hybrid venue: Seattle, Washington and on Zoom

Date: July 15, 2022

Timezone: PST – Pacific Standard Time

Previous Workshops

PrivateNLP at WSDM 2020 - view here

PrivateNLP at EMNLP 2020 - view here

PrivateNLP at NAACL 2021 - view here

Contact

For questions/queries regarding the workshop or submission: privatenlp-naacl@googlegroups.com