6 papers
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…
Optimal Variance and Covariance Estimation under Differential Privacy in the Add-Remove Model and Beyond
Shokichi Takakura, Seng Pei Liew, Satoshi Hasegawa
In this paper, we study the problem of estimating the variance and covariance of datasets under differential privacy in the add-remove model. While estimation in the swap model has…
FedDuA: Doubly Adaptive Federated Learning
Shokichi Takakura, Seng Pei Liew, Satoshi Hasegawa
Federated learning is a distributed learning framework where clients collaboratively train a global model without sharing their raw data. FedAvg is a popular algorithm for federate…
Accelerating Differentially Private Federated Learning via Adaptive Extrapolation
Shokichi Takakura, Seng Pei Liew, Satoshi Hasegawa
The federated learning (FL) framework enables multiple clients to collaboratively train machine learning models without sharing their raw data, but it remains vulnerable to privacy…