PLRV-O replaces Gaussian noise in DP-SGD with a randomized-scale Laplace distribution and claims large accuracy gains at epsilon under 1, but the privacy accounting likely underestimates the true privacy loss due to shared subsampling across model parameters.
Rothblum, and Thomas Steinke
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PLRV-O: Advancing Differentially Private Deep Learning via Privacy Loss Random Variable Optimization
PLRV-O replaces Gaussian noise in DP-SGD with a randomized-scale Laplace distribution and claims large accuracy gains at epsilon under 1, but the privacy accounting likely underestimates the true privacy loss due to shared subsampling across model parameters.