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PrivateNLP 2020 : EMNLP 2020 Workshop on Privacy and Natural Language Processing

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Link: https://sites.google.com/view/privatenlp/
 
When Nov 20, 2020 - Nov 20, 2020
Where Virtual
Abstract Registration Due Aug 21, 2020
Submission Deadline Aug 28, 2020
Notification Due Sep 25, 2020
Final Version Due Oct 10, 2020
Categories    privacy   natural language processing   NLP   security
 

Call For Papers

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:

* 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 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)
Shervin Malmasi (Amazon, USA)
Patricia Thaine (University of Toronto, Canada)


Invited Speakers:

Aaron Roth (University of Pennsylvania)
Reza Shokri (National University of Singapore)
Annabelle McIver (Macquarie University)
Mark Dras (Macquarie University)
Krishnaram Kenthapadi (Amazon AWS)


Program Committee:

Aleksei Triastcyn (École Polytechnique Fédérale de Lausanne)
Andreas Nautsch (EURECOM)
Arne Köhn (Saarland University)
Asma Eidhah Aloufi (Rochester Institute of Technology)
Balazs Pejo (Budapest University of Technology and Economics)
Benjamin Zi Hao Zhao (University of New South Wales)
Briland Hitaj (SRI International)
Christian Weinert (Technische Universität Darmstadt)
Congzheng Song (Cornell)
Dinusha Vatsalan (Data61-CSIRO)
Eleftheria Makri (Saxion University)
Elette Boyle (IDC Herzliya)
Fang Liu (University of Notre Dame)
Gerald Penn (University of Toronto)
Isar Nejadgholi (National Research Council Canada)
Jamie Hayes (University College London)
Jason Xue (University of Adelaide)
Jason Hong (Carnegie Mellon University)
Julius Adebayo (MIT)
Kambiz Ghazinour (State University of New York)
Liwei Song (Princeton)
Luca Melis (Amazon USA)
Maximin Coavoux (University of Edinburgh)
Mitra Bokaei Hosseini (St. Mary's University)
Peizhao Hu (Rochester Institute of Technology)
Sai Teja Peddinti (Google)
Shomir Wilson (Pennsylvania State University)
Tom Diethe (Amazon UK)
Travis Breaux (Carnegie Mellon University)
Xavier Ferrer (King's College London)


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

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