Privacy-Preserving UCB Decision Process Verification via zk-SNARKs

arXiv:2404.12186v1 Announce Type: new
Abstract: With the increasingly widespread application of machine learning, how to strike a balance between protecting the privacy of data and algorithm parameters and ensuring the verifiability of machine learning has always been a challenge. This study explores the intersection of reinforcement learning and data privacy, specifically addressing the Multi-Armed Bandit (MAB) problem with the Upper Confidence Bound (UCB) algorithm. We introduce zkUCB, an innovative algorithm that employs the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) …

abstract algorithm application arxiv balance challenge cs.cr cs.lg data data privacy decision intersection machine machine learning parameters privacy process reinforcement reinforcement learning strike study type verification via