EMNLP 2020 Workshop on Privacy and Natural Language Processing
PrivateNLP 2020
- URL: https://sites.google.com/view/privatenlp/
- Event Date: 2020-11-20 ~ 2020-11-20
- Abstract Submission Date: 2020-08-21
- Submission Date: 2020-08-28
EMNLP 2020 Workshop on Privacy and Natural Language Processing
Call For Papers
EMNLP PrivateNLP is a full day workshop taking place on Monday, November 20, 2020 in conjunction with EMNLP 2020.
Workshop website: https://sites.google.com/view/privatenlp/
Important Dates:
• Abstract Deadline: August 21, 2020
• Submission Deadline: August 28, 2020
• Acceptance Notification: September 25, 2020
• Camera-ready versions: October 10, 2020
• Workshop: November 20, 2020
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:
* 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
Submission Instructions:
All submissions will be double-blind peer reviewed (with author names and affiliations removed) by the program committee and judged by their relevance to the workshop themes. All submissions must be in English, as a PDF according to the ACL/EMNLP guidelines (https://www.aclweb.org/adminwiki/index.php?title=ACL_Author_Guidelines).
Submitted manuscripts must be 8 pages long for full papers, and 4 pages long for short papers. Both full and short papers can have unlimited pages for references and appendices. Extended abstracts / posters must be 2 pages. Please note that at least one of the authors of each accepted paper must register for the workshop and present the paper.
Submissions should be made as a pdf file to: https://www.softconf.com/emnlp2020/privatenlp2020/
Organizers:
Oluwaseyi Feyisetan (Amazon, USA)
Sepideh Ghanavati (University of Maine, USA)
Oleg Rokhlenko (Amazon, USA)
Patricia Thaine (University of Toronto, Canada)
Workshop website: https://sites.google.com/view/privatenlp/
Call For Papers
EMNLP PrivateNLP is a full day workshop taking place on Monday, November 20, 2020 in conjunction with EMNLP 2020.
Workshop website: https://sites.google.com/view/privatenlp/
Important Dates:
• Abstract Deadline: August 21, 2020
• Submission Deadline: August 28, 2020
• Acceptance Notification: September 25, 2020
• Camera-ready versions: October 10, 2020
• Workshop: November 20, 2020
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:
* 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
Submission Instructions:
All submissions will be double-blind peer reviewed (with author names and affiliations removed) by the program committee and judged by their relevance to the workshop themes. All submissions must be in English, as a PDF according to the ACL/EMNLP guidelines (https://www.aclweb.org/adminwiki/index.php?title=ACL_Author_Guidelines).
Submitted manuscripts must be 8 pages long for full papers, and 4 pages long for short papers. Both full and short papers can have unlimited pages for references and appendices. Extended abstracts / posters must be 2 pages. Please note that at least one of the authors of each accepted paper must register for the workshop and present the paper.
Submissions should be made as a pdf file to: https://www.softconf.com/emnlp2020/privatenlp2020/
Organizers:
Oluwaseyi Feyisetan (Amazon, USA)
Sepideh Ghanavati (University of Maine, USA)
Oleg Rokhlenko (Amazon, USA)
Patricia Thaine (University of Toronto, Canada)
Workshop website: https://sites.google.com/view/privatenlp/
Journal Promotion
|
| Acta Koreana (Acta Koreana) |
|
| Acta Via Serica (AVS) |
|
| Computers and Concrete (CAC) |
|
| Journal of Information Processing Systems (JIPS) |
|
| Journal of Internet Computing and Services (KSII - JICS) |
|
| Journal of Smart Tourism (Smart Tourism) |
|
| Membrane and Water Treatment (MWT) |
|
| Wind and Structures, An International Journal (Wind and Structures) |
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