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.
no code implementations • 27 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.