Privacy-Preserving Estimation of $k$ -Persistent Traffic in Vehicular Cyber-Physical Systems

Sun, Yu-E; Huang, He; Chen, Shigang; Zhou, You; Han, Kai; Yang, Wenjian

doi:10.1109/jiot.2019.2916349

Cited by 10 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It focuses on the management of access privileges which allow or deny access depending on user context, with access control policies that deny access to the data stored in the network for unauthorized users. To that end they analyzed a previously-studied context data access control scheme based on CP-ABE, included dynamic situations within the scope of context data and proposed an extended access control policy that reflects extended multi-dimensional context attributes [18][19][20][21].…”

Section: Figure 1 Concept Map Of Cyber-physical Systemmentioning

confidence: 99%

The Detection Data Processing Mechanism for Vehicular Cyber Physical System in IoT Environment

2021

View full text Add to dashboard Cite

With the development of the Internet of Things and big data technology, it was easy to collect personal situation information. The information collected in this way requires the user to support customized services via big data technology. However, traditional situational awareness systems request action through user cognition or provide consistent services for the specific purposes of multiple users. Therefore, this paper proposes a mechanism of Vehicular CPS with situational cognitive function that minimizes direct user intervention for user customization services. In this paper, we designed the system configuration and detailed process based on the scenario of the situation where the user is driving a car. A vector is used to provide a method for determining a dangerous water level by analyzing an abnormal state of a reception threshold with a sensor. The proposed system was analyzed by simulation. By using the authorization step that operates based on the sensor data, we were able to know that the reliability of the user is improved and that the reliable processing of the IoT service is possible. In the future, research for personal authentication and encryption is needed for more secure information processing.

show abstract

Section: Figure 1 Concept Map Of Cyber-physical Systemmentioning

confidence: 99%

The Detection Data Processing Mechanism for Vehicular Cyber Physical System in IoT Environment

2021

View full text Add to dashboard Cite

show abstract

“…e current state-of-the-art protocols for privacy-preserving traffic data collection are due to Zhou et al [12,13] and Sun et al [25,26], which provide privacy without the use of cryptographic techniques. Specifically, in their methods, every receiver maintains a physical bit array of size m (similar to a Bloom filter), and each passing vehicle sets one of the bits to "1".…”

Section: Related Workmentioning

confidence: 99%

“…Using the same data collection approach, Sun et al [25] computed the number of vehicles that persistently (in every measurement period) travel on a given road network edge. Furthermore, the same authors [26] also analyzed the number of vehicles that travel x out of T measurement periods on the network edge.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Preserving Multipoint Traffic Flow Estimation for Road Networks

Bentafat

Rathore

Bakiras

2021

Security and Communication Networks

View full text Add to dashboard Cite

Intelligent transportation systems necessitate a fine-grained and accurate estimation of vehicular traffic flows across critical paths of the underlying road network. However, such statistics should be collected in a manner that does not disclose the trajectories of individual users. To this end, we introduce a privacy-preserving protocol that leverages roadside units (RSUs) to communicate with the passing vehicles, in order to construct encrypted Bloom filters stemming from random vehicle IDs that are chosen secretly by the individual vehicles. Each Bloom filter represents the set of vehicle IDs that contacted the RSU but may also be used to estimate the traffic flow between any number of RSUs. More precisely, we designed a probabilistic model that approximates multipoint traffic flows by estimating the number of common vehicles among a given set of RSUs. Through extensive simulation experiments, we demonstrate that our protocol is very accurate—with a minor deviation from the real traffic flow—and show that it reduces the estimation error by a large factor, when compared to the current state-of-the-art approaches. Furthermore, our implementation of the underlying cryptographic primitives illustrates the feasibility, practicality, and scalability of the system.

show abstract