PrivateNLP 2020
Workshop on Privacy in Natural Language Processing

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