1 citations · 1 across the 8 of their papers we have counts for
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Escaping Iterative Parameter-Space Noise: Differentially Private Learning with a Hypernetwork
Naoki Nishikawa, Shokichi Takakura, Satoshi Hasegawa
Differentially private (DP) training of neural networks is often hindered by the large amount of noise required by gradient-based methods such as DP-SGD, which repeatedly inject hi…
Differentially Private Sampling from Distributions via Wasserstein Projection
Shokichi Takakura, Seng Pei Liew, Satoshi Hasegawa
In this paper, we study the problem of sampling from a distribution under the constraint of differential privacy (DP). Prior works measure the utility of DP sampling with density r…
DPSQL+: A Differentially Private SQL Library with a Minimum Frequency Rule
Tomoya Matsumoto, Shokichi Takakura, Shun Takagi +1
SQL is the de facto interface for exploratory data analysis; however, releasing exact query results can expose sensitive information through membership or attribute inference attac…