  This paper considers Lorden's minimax quickest change detection (QCD) problem
with a privacy constraint. The goal is to sanitize a signal to satisfy
inference privacy requirements while being able to detect a change quickly. We
show that the Generalized Likelihood Ratio (GLR) CuSum achieves asymptotic
optimality with a properly designed sanitization channel. We formulate the
design of this sanitization channel as an optimization problem, which is
however challenging to solve. We propose relaxations to the optimization
problem and develop algorithms to obtain a solution. We also consider the
privacy-aware QCD problem under a decentralized framework and propose
algorithms to solve the relaxed channel design problem under this framework.
