Vulnerability Characterization and Privacy Quantification for Cyber-Physical Systems

TL;DR

This paper introduces a personalized privacy framework for cyber-physical systems that characterizes vulnerabilities and quantifies privacy risks, enabling tailored privacy protection based on individual node preferences to improve data privacy without sacrificing utility.

Contribution

It proposes a novel vulnerability characterization model and a personalized privacy framework that adapt privacy protection to individual node preferences in CPS.

Findings

Enhanced privacy preservation by tailoring noise addition to node vulnerabilities.

Effective identification of vulnerable nodes using the Standard Vulnerability Profiling Library.

Improved balance between privacy, utility, and information loss.

Abstract

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.