| Title | Security and Privacy Trade-Offs in CPS by Leveraging Inherent Differential Privacy |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Giraldo, Jairo, Cardenas, Alvaro, Kantarcioglu, Murat |
| Conference Name | 2017 IEEE Conference on Control Technology and Applications (CCTA) |
| Keywords | control systems, Covariance matrices, cps privacy, data privacy, Databases, human factors, privacy, pubcrawl, Sensitivity, Uncertainty |
| Abstract | Cyber-physical systems are subject to natural uncertainties and sensor noise that can be amplified/attenuated due to feedback. In this work, we want to leverage these properties in order to define the inherent differential privacy of feedback-control systems without the addition of an external differential privacy noise. If larger levels of privacy are required, we introduce a methodology to add an external differential privacy mechanism that injects the minimum amount of noise that is needed. On the other hand, we show how the combination of inherent and external noise affects system security in terms of the impact that integrity attacks can impose over the system while remaining undetected. We formulate a bilevel optimization problem to redesign the control parameters in order to minimize the attack impact for a desired level of inherent privacy. |
| DOI | 10.1109/CCTA.2017.8062640 |
| Citation Key | giraldo_security_2017 |
Security and Privacy Trade-Offs in CPS by Leveraging Inherent Differential Privacy