| Vol-2573 urn:nbn:de:0074-2573-4 Copyright © 2020 for the individual papers by the papers' authors. Copyright © 2020 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).

# PrivateNLP 2020
Workshop on Privacy in Natural Language Processing

### Proceedings of the PrivateNLP 2020: Workshop on Privacy in Natural Language Processing
- Colocated with WSDM 2020

### Houston, USA, Feb 7, 2020.

Edited by

### Oluwaseyi Feyisetan *
Sepideh Ghanavati **
Oleg Rokhlenko *
Patricia Thaine ***

* Amazon,USA
** University of Maine, School of Computing and Information Science, Orono, ME, USA
** University of Toronto, Department of Computer Science, Toronto, ON, Canada

## Table of Contents

- Preface
  Oluwaseyi Feyisetan, Sepideh Ghanavati, Patricia Thaine

### Keynote

- Preserving Privacy in Analyses of Textual Data (Keynote) 1-3
  Tom Diethe, Oluwaseyi Feyisetan

### Invited Talks

- Perfectly Privacy-Preserving AI What Is It and How Do We Achieve It? (Invited Paper) 4-7
  Patricia Thaine, Gerald Penn
- Calibrating Mechanisms for Privacy Preserving Text Analysis(Invited Paper) 8-11
  Oluwaseyi Feyisetan, Borja Balle, Tom Diethe, Thomas Drake

### Full Papers and Posters

- Privacy-Aware Personalized Entity Representations for Improved User Understanding 12-20
  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 21-26
  A K M Nuhil Mehdy, Hoda Mehrpouyan
- Classification of Encrypted Word Embeddings using Recurrent Neural Networks 27-31
  Robert Podschwadt, Daniel Takabi
- Is It Possible to Preserve Privacy in the Age of AI? 32-36
  Vijayanta Jain, Sepideh Ghanavati
- Perfectly Privacy-Preserving AI What Is It and How Do We Achieve It? 37-38
  Patricia Thaine, Gerald Penn

### Other Posters

- Hyperbolic Embeddings for Preserving Privacy and Utility in Text 39-40
  Oluwaseyi Feyisetan, Tom Diethe, Thomas Drake
- Privacy-Preserving Textual Analysis via Calibrated Perturbations 41-42
  Oluwaseyi Feyisetan, Borja Balle

2020-03-10: submitted by Sepideh Ghanavati, metadata incl. bibliographic data published under Creative Commons CC0
2020-03-14: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|