ACL 2024 Workshop on Privacy and Natural Language Processing

Call For Papers

ACL PrivateNLP is a full day workshop taking place on August 15, 2024 in conjunction with ACL 2024.

Workshop website: https://sites.google.com/view/privatenlp/

Important Dates:

• Submission Deadline: May 17, 2024
• Acceptance Notification: June 17, 2024
• Camera-ready versions: July 01, 2024
• Workshop: August 15, 2024


Invited speakers
TBD


Privacy-preserving data analysis has become essential in the age of Large Language Models (LLMs) 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 LLMs 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.


Topics of interest include but are not limited to:

* Privacy in Large Language Models
* Generating privacy preserving test sets
* Inference and identification attacks
* Generating Differentially private derived data
* NLP, privacy and regulatory compliance
* Private Generative Adversarial Networks
* Privacy in Active Learning and Crowdsourcing
* Privacy and Federated Learning in NLP
* User perceptions on privatized personal data
* Auditing provenance in language models
* Continual learning under privacy constraints
* NLP and summarization of privacy policies
* Ethical ramifications of AI/NLP in support of usable privacy
* Homomorphic encryption for language models


Submissions
Accepted papers will be presented orally or as posters and included in the workshop proceedings. Submissions are open to all, and are to be submitted anonymously. All papers will be refereed through a double-blind peer review process by at least three reviewers with final acceptance decisions made by the workshop organizers.

We'll be using OpenReview - the final submission link will be specified later


Organizers
Sepideh Ghanavati, University of Maine
Abhilasha Ravichander, Allen AI
Niloofar Mireshghallah, University of Washington
Ivan Habernal, Paderborn University
Seyi Feyisetan, Amazon
Patricia Thaine, Private AI

Contact us: privatenlp24-orga@lists.uni-paderborn.de