PrivateNLP@EMNLP 2020
Second Workshop on Privacy in Natural Language Processing
Colocated with EMNLP 2020, Nov 20, 2020, Virtual, Worldwide
Colocated with EMNLP 2020, Nov 20, 2020, Virtual, Worldwide
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.
Venue: Virtual
Date: November 20, 2020
Timezone: PST – Pacific Standard Time
Oluwaseyi Feyisetan
Oracle Efficient Differentially Private Learning and Analysis
Aaron Roth (University of Pennsylvania)
Abhinav Aggarwal, Zekun Xu, Oluwaseyi Feyisetan and Nathanael Teissier (Amazon)
Reza Shokri (National University of Singapore)
A Differentially Private Text Perturbation Method Using Regularized Mahalanobis Metric [video]
Zekun Xu, Abhinav Aggarwal, Oluwaseyi Feyisetan and Nathanael Teissier (Amazon)
TextHide: Tackling Data Privacy in Language Understanding Tasks [video]
Yangsibo Huang, Zhao Song, Danqi Chen, Kai Li and Sanjeev Arora (Princeton University)
Lunch break
Mark Dras and Annabelle McIver (Macquarie University)
Identifying and Classifying Third-party Entities in Natural Language Privacy Policies [video]
Mitra Bokaie Hosseini, Pragyan K C, Irwin Reyes and Serge Egelman (St Mary's University)
Rishabh Khandelwal, Asmit Nayak, Yao Yao and Kassem Fawaz (University of Wisconsin–Madison)
Privacy in AI/ML Systems: Practical Challenges and Lessons Learned [video]
Krishnaram Kenthapadi (Amazon)
Differentially Private Language Models Benefit from Public Pre-training [video]
Gavin Kerrigan, Dylan Slack and Jens Tuyls (University of California, Irvine)
A Semantics-based Approach to Disclosure Classification in User-Generated Online Content [video]
Chandan Akiti, Anna Squicciarini, Sarah Rajtmajer (Pennsylvania State University)
Aaron Roth (University of Pennsylvania)
Reza Shokri (National University of Singapore)
Krishnaram Kenthapadi (Amazon AWS)
Annabelle McIver (Macquarie University)
Mark Dras (Macquarie University)
Submission Deadline: August 28, 2020 September 4, 2020 (11.59pm UTC-12)
Acceptance Notification: September 25, 2020
Camera-ready versions: October 10, 2020
Workshop: November 20, 2020
privatenlp-emnlp@googlegroups.com