4 citations · 7 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
Efficient Implementation of Reinforcement Learning over Homomorphic Encryption
Jihoon Suh, Takashi Tanaka
We investigate encrypted control policy synthesis over the cloud. While encrypted control implementations have been studied previously, we focus on the less explored paradigm of pr…
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…