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

  in Conferences   Posted on October 28, 2019

Conference Information

Submission Deadline Saturday 07 Dec 2019 Proceedings indexed by :
Conference Dates Feb 7, 2020 - Feb 7, 2020
Conference Address Houston, United States
Conference & Submission Link https://sites.google.com/view/wsdm-privatenlp-2020
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Call for Papers

WSDM 2020 Workshop on Privacy and Natural Language Processing

Call For Papers

WSDM PrivateNLP is a full day workshop taking place on Monday, February 7, 2020 in conjunction with WSDM 2020 in Houston, Texas.

Workshop website: https://sites.google.com/view/wsdm-privatenlp-2020

Important Dates:

• Abstract Deadline: November 30, 2019

• Submission Deadline: December 7, 2019

• Acceptance Notification: December 27, 2019

• Camera-ready versions: January 10, 2020

• Workshop: February 7, 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 Adverserial 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, formatted according to the latest 2 column ACM SIG proceedings template.

Submitted manuscripts must be 8 pages long for full papers, and 4 pages long for short papers. Both full and short papers can have 2 additional pages for references and appendices. Please note that at least one of the authors of each accepted paper must register for the workshop and present the paper in-person.

Submissions should be made as a pdf file to: https://easychair.org/conferences/?conf=privatenlp20

Organizers:

Oluwaseyi Feyisetan (Amazon, USA)

Sepideh Ghanavati (University of Maine, USA)

Oleg Rokhlenko (Amazon, USA)

Patricia Thaine (University of Toronto, Canada)

Contact: privatenlp-wsdm@googlegroups.com

Workshop website: https://sites.google.com/view/wsdm-privatenlp-2020

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