PrivateNLP@NAACL 2021
Third Workshop on Privacy in Natural Language Processing
Colocated with NAACL 2021, June 11, 2021, Virtual, Worldwide
Overview
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.
Agenda
Venue: Virtual
Date: June 11, 2021
Timezone: PST – Pacific Standard Time
08:00 - 08:10 Welcome
Sepideh Ghanavati
08:10 - 09:10 Invited Talk
Adam Dziedzic (Vector Institute)
09:10 - 09:30 Break
Morning break
09:30 - 09:50 Research Paper
An Investigation towards Differentially Private Sequence Tagging in a Federated Framework
Abhik Jana and Chris Biemann
09:50 - 10:10 Research Paper
A Privacy-Preserving Approach to Extraction of Personal Information through Automatic Annotation and Federated Learning
Rajitha Hathurusinghe, Isar Nejadgholi and Miodrag Bolic
10:10 - 10:30 Research Paper
Understanding Unintended Memorization in Language Models Under Federated Learning
Om Dipakbhai Thakkar, Swaroop Ramaswamy, Rajiv Mathews and Francoise Beaufays
10:30 - 10:45 Break
Short break
10:45 - 11:45 Invited Talk
Travis Breaux (Carnegie Mellon University)
11:45 - 12:30 Break
Lunch break
12:30 - 12:50 Research Paper
Learning and Evaluating a Differentially Private Pre-trained Language Model
Shlomo Hoory, Amir Feder, Avichai Tendler, Alon Cohen, Sofia Erell, Itay Laish, Hootan Nakhost, Uri Stemmer, Ayelet Benjamini, Avinatan Hassidim and Yossi Matias
12:50 - 13:10 Research Paper
Anonymisation Models for Text Data: State of the art, Challenges and Future Directions
Pierre Lison, Ildikó Pilán, David Sánchez, Montserrat Batet and Lilja Øvrelid
13:10 - 13:20 Break
Short break
13:20 - 13:40 Research Paper
Using Confidential Data for Domain Adaptation of Neural Machine Translation
Sohyung Kim, Arianna Bisazza and Fatih Turkmen
13:40 - 14:00 Research Paper
Private Text Classification with Convolutional Neural Networks
Samuel Adams, David Melanson and Martine De Cock
14:00 - 14:20 Research Paper
On a Utilitarian Approach to Privacy Preserving Text Generation
Zekun Xu, Abhinav Aggarwal, Oluwaseyi Feyisetan and Nathanael Teissier
14:20 - 14:50 Community discussion / Informal panel
Patricia Thaine
14:50 - 15:00 Closing remarks
Oluwaseyi Feyisetan
Invited Speakers
Travis Breaux (Carnegie Mellon University)
Adam Dziedzic (Vector Institute and The University of Toronto)
Key Dates
Submission Deadline:
March 15, 2021March 22, 2021 (11.59pm UTC-12)Acceptance Notification: April 15, 2021
Camera-ready versions: April 26, 2021
Workshop: June 11, 2021
Contact
privatenlp-naacl@googlegroups.com