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…
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…
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.…
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…