PCCO - Privacy-preserving Collaborative Control and Optimization in VANETs
Final Report Abstract
Vehicular Ad hoc Network (VANET) is one of the fundamental technologies for the efficient, green, and safe intelligent transportation system. It provides a wireless communication channel for vehicles to extract, share, and utilize real-time traffic information, and therefore enables the vehicles to collaborate with other vehicles as well as the infrastructures to improve working efficiency and accommodate personalized demands. Since the collaboration relies on the share of personal data, including sensitive information such as real-time location, driving status, and et al, privacy concerns have been the major barrier that restricts the wide application of VANET. To fix this issue, smart strategies are desired to reconcile the contradiction between data sharing and privacy leakage. Our main objective of this project is to explore information-theoretic methods that can help to reduce information leakage in the process of data sharing. Focusing on data transmission across wireless communication channels, we have conducted a series of studies on privacy preserving of static and dynamic data, including (i) smart event-based transmission policies for remote state estimation in the presence of passive eavesdroppers. The method can be applied to strictly and marginally stable systems. The optimal transmission threshold can be easily derived by a simple bisection method. (ii) design of resilient transmission policies against active eavesdroppers which are capable of hacking the local sensor or attacking the acknowledgment signal. Informationtheoretic and encoding methods have been explored. Algorithms that can efficiently solve the corresponding optimization problems have been proposed. (iii) an optimal joint design of control and scheduling strategies in remote control problems with DoS attacks. We have shown the optimality of a separated design such that the problem can be solved by a simple Q-learning. (iv) a multi-dimensional data-privacy algorithm for the publication of data with correlations. An optimal noise-adding mechanism is proposed to minimize the disclosure probability. These strategies can be applied to many scenarios in intelligent transportation systems, such as cooperative cruise control, vehicle collision warning, safe distance warning, dissemination of road information, path planning, safe driving monitoring, and et al. Beyond that, the methods proposed can be extended and applied to the privacy preservation of many other networked systems including smart grid systems, industrial process control, intelligent building systems, and et al.
Publications
- 2020. Optimal event-triggered transmission scheduling for privacy-preserving wireless state estimation. International Journal of Robust and Nonlinear Control, 30(11), pp. 4205-4224
Lu, J., Leong, A.S. and Quevedo, D.E.
(See online at https://doi.org/10.1002/rnc.4910) - 2020. Privacy-Preserving Correlated Data Publication: Privacy Analysis and Optimal Noise Design. IEEE Transactions on Network Science and Engineering
Sun, M., Zhao, C., He, J., Cheng, P. and Quevedo, D.
(See online at https://doi.org/10.1109/TNSE.2020.3044590) - 2022. Stealthy hacking and secrecy of controlled state estimation systems with random dropouts. IEEE Transactions on Automatic Control
Lu, J., Quevedo, D., Gupta, V. and Dey, S.
(See online at https://doi.org/10.1109/TAC.2021.3131434)
