| Title | Vulnerability Characterization and Privacy Quantification for Cyber-Physical Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Bhattacharjee, Arpan, Badsha, Shahriar, Hossain, Md Tamjid, Konstantinou, Charalambos, Liang, Xueping |
| Conference Name | 2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing Communications (GreenCom) and IEEE Cyber, Physical Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics) |
| Keywords | Computational modeling, cps privacy, Cyber-physical systems, Cyber-Physical Systems (CPS), data privacy, Differential privacy, Differential Privacy (DP), human factors, Personalized Differential Privacy, privacy, pubcrawl, Publishing, social computing, vulnerabilities |
| 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 over-protecting 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. |
| DOI | 10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics53846.2021.00045 |
| Citation Key | bhattacharjee_vulnerability_2021 |
Vulnerability Characterization and Privacy Quantification for Cyber-Physical Systems