  The privacy aspect of state estimation algorithms has been drawing high
research attention due to the necessity for a trustworthy private environment
in cyber-physical systems. These systems usually engage cloud-computing
platforms to aggregate essential information from spatially distributed nodes
and produce desired estimates. The exchange of sensitive data among semi-honest
parties raises privacy concerns, especially when there are coalitions between
parties. We propose two privacy-preserving protocols using Kalman filter and
partially homomorphic encryption of the measurements and estimates while
exposing the covariances and other model parameters. We prove that the proposed
protocols achieve satisfying computational privacy guarantees against various
coalitions based on formal cryptographic definitions of indistinguishability.
We evaluate the proposed protocols to demonstrate their efficiency using data
from a real testbed.
