Search Results for author: Victor Rühle

Found 4 papers, 0 papers with code

Bayesian Estimation of Differential Privacy

no code implementations10 Jun 2022 Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor Rühle, Andrew Paverd, Mohammad Naseri, Boris Köpf, Daniel Jones

Our Bayesian method exploits the hypothesis testing interpretation of differential privacy to obtain a posterior for $\varepsilon$ (not just a confidence interval) from the joint posterior of the false positive and false negative rates of membership inference attacks.

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.

Memorization Privacy Preserving

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.

Analyzing Information Leakage of Updates to Natural Language Models

no code implementations17 Dec 2019 Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Rühle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, Marc Brockschmidt

To continuously improve quality and reflect changes in data, machine learning applications have to regularly retrain and update their core models.

Language Modelling

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