  Cyber-physical systems (CPS) data privacy protection during sharing,
aggregating, and publishing is a challenging problem. Several privacy
protection mechanisms have been developed in the literature to protect
sensitive data from adversarial analysis and eliminate the risk of
re-identifying the original properties of shared data. However, most of the
existing solutions have drawbacks, such as (i) lack of a proper vulnerability
characterization model to accurately identify where privacy is needed, (ii)
ignoring data providers privacy preference, (iii) using uniform privacy
protection which may create inadequate privacy for some provider while
overprotecting others, and (iv) lack of a comprehensive privacy quantification
model assuring data privacy-preservation. To address these issues, we propose a
personalized privacy preference framework by characterizing and quantifying the
CPS vulnerabilities as well as ensuring privacy. First, we introduce a Standard
Vulnerability Profiling Library (SVPL) by arranging the nodes of an energy-CPS
from maximum to minimum vulnerable based on their privacy loss. Based on this
model, we present our personalized privacy framework (PDP) in which Laplace
noise is added based on the individual node's selected privacy preferences.
Finally, combining these two proposed methods, we demonstrate that our privacy
characterization and quantification model can attain better privacy
preservation by eliminating the trade-off between privacy, utility, and risk of
losing information.
