Privacy-Preserving Transportation Traffic Measurement in Intelligent Cyber-physical Road Systems

Zhou, Yingying; Zhang, Mo; Xiao, Qingjun; Chen, Shigang; Yin, Yafeng

doi:10.1109/tvt.2015.2436395

Cited by 58 publications

(26 citation statements)

References 21 publications

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“…A CPS-based solution was proposed to overcome the physical and cyber interferences in order to improve these two characters. Zhou et al [24] considered the privacy protection in CPS when the traffic state are collected. In this paper, they proposed a novel scheme which can gather the traffic flow data with protecting the privacy of each vehicle.…”

Section: Related Work a Security In Cptsmentioning

confidence: 99%

Jamming and Eavesdropping Defense in Green Cyber–Physical Transportation Systems Using a Stackelberg Game

Wang

Yuan

Miyazaki

et al. 2018

IEEE Trans. Ind. Inf.

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This paper studies the secure transmission rate issue between sensors and the remote controller to defend the jamming and eavesdropping attacks in Green Cyber-Physical Transportation Systems (GCPTS). In this system, the traffic sensor transmits the transportation state information to the remote controller via wireless networks. Due to the broadcast characteristics of wireless communication, the systems are vulnerable to the eavesdropping and jamming attacks. In this paper, we study how to maximize the secure transmission rate between sensors and the controller with the presence of malicious eavesdropper and jammer. Specifically, the malicious jammer is smart and can choose the optimal power strategy to maximize the side effect with the knowledge of sensor's transmission power. For the purpose of achieving the maximum utility, the optimal strategy is determined via adjusting the sensor's transmission power according to the control feedback conditions. We consider the single-antenna model and the multi-antenna model to formulate this problem as an optimization problem based on the Stackelberg game. We then prove the existence of Stackelberg equilibrium via the interaction between the sensor and the jammer. Moreover, we present two algorithms to obtain the optimal transmission strategy, i.e., stochastic algorithm with feedback (SAF) and renewed intelligent simulated annealing (RISA). Finally, extensive simulations and trace experimental results are presented to verify our theoretical analysis.

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Section: Related Work a Security In Cptsmentioning

confidence: 99%

Jamming and Eavesdropping Defense in Green Cyber–Physical Transportation Systems Using a Stackelberg Game

Wang

Yuan

Miyazaki

et al. 2018

IEEE Trans. Ind. Inf.

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“…In [42], the authors considered the anonymous aggregation of data (e.g. for traffic planning) without exposing any individual data by using bit masks and maximum likelihood estimators.…”

Section: F Securitymentioning

confidence: 99%

Vehicular cloud computing in the dawn of 5G

Falchetti

Azurdia-Meza

Céspedes

2015

2015 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

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In this work we introduce some of the key ideas of vehicular cloud computing applied to 5G cellular networks. We present some of the challenges and solutions that are being devised for these networks, with a special focus on the lower layers of 5G cellular systems. The main challenges in vehicular cloud computing examined in this paper are mobility, latency, network topology, network security, and reliability.

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“…For example, the IntelliDrive system in United States allows cars on the road to communicate over short distances in real-time through roadside facilities. Vehicle identification codes are also obtained when vehicles pass the system's sensors; this data is then used to measure traffic flows [1]. Although the installation of GPS in specific vehicles (e.g., buses for public transport) allows for the collection of information related to their driving dynamics, these systems have been installed by transportation enterprises and the data is only used internally.…”

Section: Introductionmentioning

confidence: 99%

Data Re-Identification—A Case of Retrieving Masked Data from Electronic Toll Collection

Huang

Lin

2019

Symmetry

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With the growth of big data and open data in recent years, the importance of data anonymization is increasing. Original data need to be anonymized to prevent personal identification from being revealed before being released to the public. There is a growing variety of de-identification methods which have been proposed to reduce the privacy issues, however, there is still much to be improved. The purpose of this study is to demonstrate the possibilities of re-identification from masked data, and to compare the pros and cons of different de-identification methods. A set of electronic toll collection data from Taiwan was used and we successfully re-identified vehicles with specific patterns. Four de-identification methods were performed and finally we compared the strengths and weaknesses of these methods and evaluated their appropriateness.

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Privacy-Preserving Transportation Traffic Measurement in Intelligent Cyber-physical Road Systems

Cited by 58 publications

References 21 publications

Jamming and Eavesdropping Defense in Green Cyber–Physical Transportation Systems Using a Stackelberg Game

Jamming and Eavesdropping Defense in Green Cyber–Physical Transportation Systems Using a Stackelberg Game

Vehicular cloud computing in the dawn of 5G

Data Re-Identification—A Case of Retrieving Masked Data from Electronic Toll Collection

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