Search Results for author: Robert Sim

Found 8 papers, 0 papers with code

Privacy Regularization: Joint Privacy-Utility Optimization in LanguageModels

no code implementations NAACL 2021 FatemehSadat Mireshghallah, Huseyin Inan, Marcello Hasegawa, Victor R{\"u}hle, Taylor Berg-Kirkpatrick, Robert Sim

In this work, we introduce two privacy-preserving regularization methods for training language models that enable joint optimization of utility and privacy through (1) the use of a discriminator and (2) the inclusion of a novel triplet-loss term.

On Privacy and Confidentiality of Communications in Organizational Graphs

no code implementations27 May 2021 Masoumeh Shafieinejad, Huseyin Inan, Marcello Hasegawa, Robert Sim

We propose a model that captures the correlation in the social network graph, and incorporates this correlation in the privacy calculations through Pufferfish privacy principles.

Language Modelling

Privacy Regularization: Joint Privacy-Utility Optimization in Language Models

no code implementations12 Mar 2021 FatemehSadat Mireshghallah, Huseyin A. Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, Robert Sim

In this work, we introduce two privacy-preserving regularization methods for training language models that enable joint optimization of utility and privacy through (1) the use of a discriminator and (2) the inclusion of a triplet-loss term.

Training Data Leakage Analysis in Language Models

no code implementations14 Jan 2021 Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim

It has been demonstrated that strong performance of language models comes along with the ability to memorize rare training samples, which poses serious privacy threats in case the model is trained on confidential user content.

Conversations with Documents. An Exploration of Document-Centered Assistance

no code implementations27 Jan 2020 Maartje ter Hoeve, Robert Sim, Elnaz Nouri, Adam Fourney, Maarten de Rijke, Ryen W. White

Our contributions are three-fold: (1) We first present a survey to understand the space of document-centered assistance and the capabilities people expect in this scenario.

Cannot find the paper you are looking for? You can Submit a new open access paper.