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ORCIDPrivateNLP@WSDM 2020: Houston, TX, USA
- Oluwaseyi Feyisetan, Sepideh Ghanavati, Oleg Rokhlenko, Patricia Thaine:
Proceedings of the PrivateNLP 2020: Workshop on Privacy in Natural Language Processing - Colocated with WSDM 2020, Houston, USA, Feb 7, 2020. CEUR Workshop Proceedings 2573, CEUR-WS.org 2020
Keynote
- Tom Diethe, Oluwaseyi Feyisetan:
Preserving Privacy in Analyses of Textual Data. 1-3
Invited Talks
- Patricia Thaine, Gerald Penn:
Perfectly Privacy-Preserving AI What Is It and How Do We Achieve It? 4-7 - Oluwaseyi Feyisetan, Borja Balle, Tom Diethe, Thomas Drake:
Calibrating Mechanisms for Privacy Preserving Text Analysis. 8-11
Full Papers and Posters
- Levi Melnick, Hussein Elmessilhy, Vassilis Polychronopoulos, Gilsinia Lopez, Yuancheng Tu, Omar Zia Khan, Ye-Yi Wang, Chris Quirk:
Privacy-Aware Personalized Entity Representations for Improved User Understanding. 12-20 - A. K. M. Nuhil Mehdy, Hoda Mehrpouyan:
A User-Centric and Sentiment Aware Privacy-Disclosure Detection Framework based on Multi-input Neural Network. 21-26 - Robert Podschwadt, Daniel Takabi:
Classification of Encrypted Word Embeddings using Recurrent Neural Networks. 27-31 - Vijayanta Jain, Sepideh Ghanavati:
Is It Possible to Preserve Privacy in the Age of AI? 32-36 - Patricia Thaine, Gerald Penn:
Perfectly Privacy-Preserving AI What Is It and How Do We Achieve It? 37-38
Other Posters
- Oluwaseyi Feyisetan, Tom Diethe, Thomas Drake:
Hyperbolic Embeddings for Preserving Privacy and Utility in Text. 39-40 - Oluwaseyi Feyisetan, Borja Balle:
Privacy-Preserving Textual Analysis via Calibrated Perturbations. 41-42
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