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PrivateNLP 2021 : NAACL 2021 Workshop on Privacy and Natural Language Processing

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Link: https://sites.google.com/view/privatenlp
 
When Jun 11, 2021 - Jun 11, 2021
Where Virtual
Submission Deadline Mar 22, 2021
Notification Due Apr 15, 2021
Final Version Due Apr 26, 2021
 

Call For Papers

NAACL 2021 Workshop on Privacy and Natural Language Processing

Call For Papers

NAACL PrivateNLP is a full day workshop taking place on Friday, June 11, 2021 in conjunction with NAACL 2021.

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

Important Dates:

• Submission Deadline: March 22, 2021
• Acceptance Notification: April 15, 2021
• Camera-ready versions: April 26, 2021
• Workshop: June 11, 2021

Invited speakers

• Travis Breaux (Carnegie Mellon University)
• Adam Dziedzic (Vector Institute and The University of Toronto)

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.


Topics of interest include but are not limited to:

* Privacy preserving machine learning for 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


Submission details:

Two types of submissions are invited: full papers and short papers.

Full papers should not exceed eight (8) pages of text, plus unlimited references. Final versions of full papers will be given one additional page of content (up to 9 pages) so that reviewers' comments can be taken into account.

Short papers may consist of up to four (4) pages of content, plus unlimited references. Upon acceptance, short papers will still be given up to five (5) content pages in the proceedings.

See the guidelines here: https://2021.naacl.org/calls/style-and-formatting/

Submissions should be made as a pdf file to: https://www.softconf.com/naacl2021/privatenlp2021/


Organizers:

Oluwaseyi Feyisetan (Amazon, USA)
Sepideh Ghanavati (University of Maine, USA)
Shervin Malmasi (Amazon, USA)
Patricia Thaine (University of Toronto, Canada)


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

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