1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CR2021
Efficient decision tree training with new data structure for secure multi-party computation
Koki Hamada, Dai Ikarashi, Ryo Kikuchi +1
We propose a secure multi-party computation (MPC) protocol that constructs a secret-shared decision tree for a given secret-shared dataset. The previous MPC-based decision tree tra…
cs.CR2021
Adam in Private: Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation
Nuttapong Attrapadung, Koki Hamada, Dai Ikarashi +5
Privacy-preserving machine learning (PPML) aims at enabling machine learning (ML) algorithms to be used on sensitive data. We contribute to this line of research by proposing a fra…
cs.CR2015★ 1 cited
k-anonymous Microdata Release via Post Randomisation Method
Dai Ikarashi, Ryo Kikuchi, Koji Chida +1
The problem of the release of anonymized microdata is an important topic in the fields of statistical disclosure control (SDC) and privacy preserving data publishing (PPDP), and ye…