Towards Privacy-Preserving Wi-Fi Monitoring for Road Traffic Analysis

Fuxjaeger, Paul; Ruehrup, Stefan; Paulin, Thomas; Rainer, Bernd

doi:10.1109/mits.2016.2573341

Cited by 19 publications

(14 citation statements)

References 5 publications

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“…To the best of our knowledge, this is the first work that studies persistent traffic measurement (as defined in Section 2.1) through vehicle-to-infrastructure communications. There are prior privacy-preserving approaches for measuring point-to-point traffic [30], [32] or measuring travel time [7]. But there is no prior work that measures persistent point-to-point traffic under the same model in Section 2.2.…”

Section: Simulationmentioning

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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Section: Simulationmentioning

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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“…Demir [9] proposed a multiple hashing of MAC addresses. Fuxjäger et al [10] show that brute-force attacks on just hashed MAC addresses are quite simple, and, thus suggest a truncated and hashed MAC address approach with a higher level of privacy. Finally, Martin et al [4] recently showed that even the more advanced technique of MAC address randomization can be attacked with a 100 % success ratio.…”

Section: Related Workmentioning

confidence: 99%

“…Fuxjäger et al [10] report on traffic jam analysis experiments on Austrian roads, but they used a more expensive equipment with external antennae.…”

Section: Related Workmentioning

confidence: 99%

Raspberry Pi as an Inexpensive Platform for Real-Time Traffic Jam Analysis on the Road

Baumgartl

Müller

2018

Annals of Computer Science and Information Systems

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Using mobile phones for accessing the Internet has become a standard use case of such devices, nowadays even more important than the good old phone call. WiFi at home or public ones allow for a low-cost or even unpaid access to the virtual world of the Internet. But, as we will show, this is only true to some degree in terms of monetary cost. One thing we're paying a lot with is the loss of our privacy. In this paper, we will show how easily and cheap potential attackers can track your mobile phone and, thus, you via data it sends all the time, so-called probe requests. Additionally we show by experimental data how this tracking can be used for traffic jam analysis on roads.

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“…However (Marx et al, 2018) notes that due to a small pre-image space for MAC addresses, all SHA256 hashed MAC addresses could be recovered in 13 minutes 22 seconds. Despite MAC addresses containing a 48-bit search space, with only 0.1% of OUI manufacturer prefixes for MAC addresses being allocated (Fuxjaeger et al, 2016) it is possible to represent all MAC addresses in a digest of 39 bits. Furthermore, due to the unequal usage of various OUI prefixes (Demir et al, 2014), it is possible to represent 50% coverage in 31 bits, 90% coverage in 33 bits and 99% coverage in 34 bits respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, due to the unequal usage of various OUI prefixes (Demir et al, 2014), it is possible to represent 50% coverage in 31 bits, 90% coverage in 33 bits and 99% coverage in 34 bits respectively. Hence, (Fuxjaeger et al, 2016) proposes simply removing the OUI manufacturer prefix from MAC address before storage. However, (Martin et al, 2016) finds that contiguous address blocks are allocated to specific devices meaning the residual NIC suffix of the MAC address can similarly be used for identification attacks.…”

Section: Introductionmentioning

confidence: 99%

Practical Hash-based Anonymity for MAC Addresses

Ali¹,

Dyo

2020

Proceedings of the 17th International Joint Conference on E-Business and Telecommunications

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Given that a MAC address can uniquely identify a person or a vehicle, continuous tracking over a large geographical scale has raised serious privacy concerns amongst governments and the general public. Prior work has demonstrated that simple hash-based approaches to anonymization can be easily inverted due to the small search space of MAC addresses. In particular, it is possible to represent the entire allocated MAC address space in 39 bits and that frequency-based attacks allow for 50% of MAC addresses to be enumerated in 31 bits. We present a practical approach to MAC address anonymization using both computationally expensive hash functions and truncating the resulting hashes to allow for k-anonymity. We provide an expression for computing the percentage of expected collisions, demonstrating that for digests of 24 bits it is possible to store up to 168, 617 MAC addresses with the rate of collisions less than 1%. We experimentally demonstrate that a rate of collision of 1% or less can be achieved by storing data sets of 100 MAC addresses in 13 bits, 1, 000 MAC addresses in 17 bits and 10, 000 MAC addresses in 20 bits. a https://orcid.

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Towards Privacy-Preserving Wi-Fi Monitoring for Road Traffic Analysis

Cited by 19 publications

References 5 publications

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

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

Raspberry Pi as an Inexpensive Platform for Real-Time Traffic Jam Analysis on the Road

Practical Hash-based Anonymity for MAC Addresses

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