2 citations · 3 across the 6 of their papers we have counts for
6 papers
Experimental Examination of Secure Two-Party Controller Computation
Kaoru Teranishi, Jihoon Suh, Takashi Tanaka
A secure two-party computation protocol for running dynamic controllers over secret sharing has recently been proposed. Unlike encrypted control schemes based on homomorphic encryp…
Privacy-Preserving Fully Distributed Gaussian Process Regression
Yeongjun Jang, Kaoru Teranishi, Jihoon Suh +1
Although distributed Gaussian process regression (GPR) enables multiple agents to jointly learn a model of the target function, its collaborative nature poses a risk of private dat…
Relative Entropy Regularized Reinforcement Learning for Efficient Encrypted Policy Synthesis
Jihoon Suh, Yeongjun Jang, Kaoru Teranishi +1
We propose an efficient encrypted policy synthesis to develop privacy-preserving model-based reinforcement learning. We first demonstrate that the relative-entropy-regularized rein…
Faithful and Privacy-Preserving Implementation of Average Consensus
Kaoru Teranishi, Kiminao Kogiso, Takashi Tanaka
We propose a protocol based on mechanism design theory and encrypted control to solve average consensus problems among rational and strategic agents while preserving their privacy.…
Client-Aided Secure Two-Party Computation of Dynamic Controllers
Kaoru Teranishi, Takashi Tanaka
In this paper, we propose a secure two-party computation protocol for dynamic controllers using a secret sharing scheme. The proposed protocol realizes outsourcing of controller co…
Encrypted Computation of Collision Probability for Secure Satellite Conjunction Analysis
Jihoon Suh, Michael Hibbard, Kaoru Teranishi +3
The computation of collision probability () is crucial for space environmentalism and sustainability by providing decision-making knowledge that can prevent collisio…