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PrivateNLP 2022 : NAACL 2022 Workshop on Privacy and Natural Language Processing

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Link: https://sites.google.com/view/privatenlp/
 
When Jul 15, 2022 - Jul 15, 2022
Where Seattle
Submission Deadline Apr 20, 2022
Notification Due May 6, 2022
Final Version Due May 20, 2022
Categories    privacy   natural language processing
 

Call For Papers

NAACL 2022 Fourth Workshop on Privacy and Natural Language Processing

Call For Papers

NAACL PrivateNLP is a full day hybrid workshop taking place on Friday, July 15, 2022 in conjunction with NAACL 2022.

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

Venue: Seattle, and on Zoom

Important Dates:

• Submission Deadline: April 20, 2022
• Acceptance Notification: May 06, 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)


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:
Long papers: https://aclrollingreview.org/cfp#long-papers
Short papers: https://aclrollingreview.org/cfp#short-papers

Submissions should be made as a pdf file to:
https://www.softconf.com/naacl2022/PrivateNLP2022/


Organizers:

Oluwaseyi Feyisetan (Meta, USA)
Sepideh Ghanavati (University of Maine, USA)
Patricia Thaine (University of Toronto, Canada)
Ivan Habernal (Technische Universität Darmstadt, Germany)
Fatemehsadat Mireshghallah (University of California, San Diego)


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

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