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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:
http://ceur-ws.org/Vol-2573
DBLP:
conf/wsdm/2020privatenlp
DBLP bib files:
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dblp
Proceedings of the PrivateNLP 2020: Workshop on Privacy in Natural Language Processing - Colocated with WSDM 2020, Houston, USA, Feb 7, 2020

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Preserving Privacy in Analyses of Textual Data
Tom Diethe | Oluwaseyi Feyisetan

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Perfectly Privacy-Preserving AI What Is It and How Do We Achieve It?
Patricia Thaine | Gerald Penn

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Calibrating Mechanisms for Privacy Preserving Text Analysis
Oluwaseyi Feyisetan | Borja Balle | Tom Diethe | Thomas Drake

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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

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A User-Centric and Sentiment Aware Privacy-Disclosure Detection Framework based on Multi-input Neural Network
A. K. M. Nuhil Mehdy | Hoda Mehrpouyan

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Classification of Encrypted Word Embeddings using Recurrent Neural Networks
Robert Podschwadt | Daniel Takabi

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Is It Possible to Preserve Privacy in the Age of AI?
Vijayanta Jain | Sepideh Ghanavati

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Perfectly Privacy-Preserving AI What Is It and How Do We Achieve It?
Patricia Thaine | Gerald Penn

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Hyperbolic Embeddings for Preserving Privacy and Utility in Text
Oluwaseyi Feyisetan | Tom Diethe | Thomas Drake

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Privacy-Preserving Textual Analysis via Calibrated Perturbations
Oluwaseyi Feyisetan | Borja Balle