Privacy preserving origin-destination flow measurement in vehicular cyber-physical systems

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

doi:10.1109/cpsna.2013.6614243

Cited by 15 publications

(6 citation statements)

References 17 publications

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“…The use of personal data and the stringent pressure placed by governments and agencies on differential privacy preservation has spurred a flurry of research to prevent models from revealing sensitive data from their training instances [ 197 , 221 ]. Within the ITS domain, it is possible to find many areas in which privacy preservation has recently been a subject of intense research: from origin-destination flow estimation [ 222 ] to route planners [ 223 , 224 ], or pattern mining [ 225 ], a glance at recent literature reveals the momentum this topic has acquired lately. In any of these examples data are available as a result of the sensing pervasiveness (specially in the case of VANETs) and the capture of user data.…”

Section: Emerging Ai Areas Towards Actionable Itsmentioning

confidence: 99%

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Laña

Medina

Vlahogianni

et al. 2021

Sensors

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Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.

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Section: Emerging Ai Areas Towards Actionable Itsmentioning

confidence: 99%

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Laña

Medina

Vlahogianni

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…e distributed key sharing algorithm assures that trip reports are available once k vehicles have the same trip with matching O-D and start-end times. Zhou et al [24] measured origin-destination traffic flows using commutative one-way hash functions that are constructed from an RSA-like cryptosystem. Although the hash function hides the vehicle's ID from the RSU, it fails to protect the privacy of the trajectory when all the data are aggregated at the centralized server.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Preserving Multipoint Traffic Flow Estimation for Road Networks

Bentafat

Rathore

Bakiras

2021

Security and Communication Networks

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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.

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“…Lou and Yin proposed a model to infer point-to-point statistics from single-point data [14], but it has limited practicability for its high computation overhead. To solve this problem, Zhou et al studied the point-to-point and multi-point traffic measurement problems respectively in [31] and [30], which can balance the privacy preservation and measurement accuracy. We study a new problem in this work, which aims to measure the persistent traffic volume.…”

Section: Related Workmentioning

confidence: 99%

Persistent Traffic Measurement through Vehicle-to-Infrastructure Communications in Cyber-Physical Road Systems

Sun

Huang

Chen

et al. 2019

IEEE Trans. on Mobile Comput.

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Measuring traffic volume in a road system has important applications in transportation engineering. The connected vehicle technologies integrate wireless communications and computers into transportation systems, allowing wireless data exchanges between vehicles and road-side equipment, and enabling large-scale, sophisticated traffic measurement. This paper investigates the problem of persistent traffic measurement, which was not adequately studied in the prior art, particularly in the context of intelligent vehicular networks. We propose three estimators for privacy-preserving persistent traffic measurement: one for point traffic, one for point-to-point traffic, and another for three-point traffic. After that, we present a general framework to measure persistent traffic that go through more than three locations. The estimators are mathematically derived from the join result of traffic records, which are produced by the electronic roadside units with privacy-preserving data structures. We evaluate our estimation methods using simulations based on both real transportation traffic data and synthetic data. The numerical results demonstrate the effectiveness of the proposed methods in producing high measurement accuracy and allowing accuracy-privacy tradeoff through parameter setting.

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Privacy preserving origin-destination flow measurement in vehicular cyber-physical systems

Cited by 15 publications

References 17 publications

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Privacy-Preserving Multipoint Traffic Flow Estimation for Road Networks

Persistent Traffic Measurement through Vehicle-to-Infrastructure Communications in Cyber-Physical Road Systems

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