Proceedings of the PrivateNLP 2020: Workshop on Privacy in Natural Language Processing - Colocated with WSDM 2020, Houston, USA, Feb 7, 2020
Oluwaseyi Feyisetan, Sepideh Ghanavati, Oleg Rokhlenko, Patricia Thaine (Editors)
- Anthology ID:
- 2020.wsdm_workshop-2020privatenlp
- Year:
- 2020
- Venue:
- wsdm_workshop
- Publisher:
- CEUR-WS.org
- URL:
- https://ceur-ws.org/Vol-2573
- DBLP:
- conf/wsdm/2020privatenlp
Preserving Privacy in Analyses of Textual Data
Tom Diethe
|
Oluwaseyi Feyisetan
Perfectly Privacy-Preserving AI What Is It and How Do We Achieve It?
Patricia Thaine
|
Gerald Penn
Calibrating Mechanisms for Privacy Preserving Text Analysis
Oluwaseyi Feyisetan
|
Borja Balle
|
Tom Diethe
|
Thomas Drake
Privacy-Aware Personalized Entity Representations for Improved User Understanding
Levi Melnick
|
Hussein Elmessilhy
|
Vassilis Polychronopoulos
|
Gilsinia Lopez
|
Yuancheng Tu
|
Omar Zia Khan
|
Ye-Yi Wang
|
Chris Quirk
A User-Centric and Sentiment Aware Privacy-Disclosure Detection Framework based on Multi-input Neural Network
A. K. M. Nuhil Mehdy
|
Hoda Mehrpouyan
Classification of Encrypted Word Embeddings using Recurrent Neural Networks
Robert Podschwadt
|
Daniel Takabi
Is It Possible to Preserve Privacy in the Age of AI?
Vijayanta Jain
|
Sepideh Ghanavati
Perfectly Privacy-Preserving AI What Is It and How Do We Achieve It?
Patricia Thaine
|
Gerald Penn
Hyperbolic Embeddings for Preserving Privacy and Utility in Text
Oluwaseyi Feyisetan
|
Tom Diethe
|
Thomas Drake
Privacy-Preserving Textual Analysis via Calibrated Perturbations
Oluwaseyi Feyisetan
|
Borja Balle