Article

Privacy-Preserving Traffic Monitoring with False Report Filtering via Fog-assisted Vehicular Crowdsensing

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Abstract

Traffic monitoring system empowers cloud server and drivers to collect real-time driving information and acquire traffic conditions. However, drivers are more interested in local traffic, and sending driving reports to a remote cloud server consumes a heavy bandwidth and incurs an increased response delay. Recently, fog computing is introduced to provide location-sensitive and latency-aware local data management in vehicular crowdsensing, but it also raises new privacy concerns because drivers' information could be disclosed. Although these messages are encrypted before transmission, malicious drivers can upload false reports to sabotage the systems, and filtering out false encrypted reports remains a challenging issue. To address the problems, we define a new security model and propose a privacy preserving traffic monitoring scheme. Specifically, we utilize short group signature to authenticate drivers in a conditionally anonymous way, adopt a range query technique to acquire driving information in a privacy-preserving way, and integrate it to the construction of a weighted proximity graph at each fog node through a WiFi challenge handshake to filter out false reports. Moreover, we use variant Bloom filters to achieve fast traffic conditions storage and retrieval. Finally, we prove the security and privacy, evaluate the performance with real-world cloud servers.

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... For example, a driver colludes with SP to identify an anonymous rider in a RHS. A group of colluding drivers send false traffic reports to an RSU and fake a traffic jam [141]. ...
... After a driver sends a report to the cloud server, the cloud serve performs soundness verification on the report to filter out fake information sent by malicious drivers. PAM [141] is a privacy-preserving traffic monitoring scheme with a false report filtering function. It utilizes BBS signature [186] to offer anonymous authentication and conditional identity revealing. ...
... Instead of asking contributing drivers to upload traffic reports periodically, virtual trip lines are deployed to avoid traffic updates from sensitive locations. PAM [141] adopts range query processing [197] and variant Bloom filters for local RSUs to learn traffic conditions from encrypted driving reports without sacrificing drivers' location privacy. Each contributing driver encodes her/his location and speed by prefix encoding [198] and then inserts the encoded information with a random number into a Bloom filter, which is an index. ...
Article
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Vehicular networks allow billions of vehicular users to be connected to report and exchange real-time data for offering various services, such as navigation, ride-hailing, smart parking, traffic monitoring, and vehicular digital forensics. Fifth generation (5G) is a new radio access technology with greater coverage, accessibility, and higher network density. 5G-supported Vehicular Networks (5GVNs) have attracted plenty of attention from both academia and industry. Geared with new features, they are expected to revolutionize the mobility ecosystem to empower a portfolio of new services. Meanwhile, the development of such communication capabilities, along with the development of sensory devices and the enhancement of local computing powers, have lead to an inevitable reality of massive data (e.g., identity, location, and trajectory) collection from vehicular users. Unfortunately, 5GVN are still confronted with a variety of privacy threats. Such threats are targeted at users’ data, identity, location, and trajectory. If not properly handled, such threats will cause unimaginable consequences to users. In this survey, we first review the state-of-the-art of survey papers. Next, we introduce the architecture, features, and services of 5GVN, followed by the privacy objectives of 5GVN and privacy threats to 5GVN. Further, we present existing privacy-preserving solutions and analyze them in-depth. Finally, we define some future research directions to draw more attention and down-toearth efforts into this new architecture and its privacy issues.
... However, schemes 14,15 only consider protecting the driver's privacy information in the communication process, but lack consideration of protecting the driver's privacy information when KGC processes the vehicle driving information 16 . Recently, schemes 17,18 try to improve this weakness, but their computation and communication costs is heavy. Our scheme also pays attention to this problem, especially in the traffic monitoring system, KGC can easily obtain the driver's privacy information by processing the vehicle's driving information. ...
... In addition, drivers are more concerned about the traffic conditions nearby and the planning road. If the vehicle obtains the local traffic conditions from the remote KGC 19 , it will consume a heavy bandwidth and increase the communication delay 18 . Recently, fog computation has been applied to VANET 20,21,22 . ...
... In this section, the performance including computation cost and communication cost are presented by comparing with two traffic monitoring schemes 18,32 . ...
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In the traffic monitoring system, the transportation department generates the traffic map to report the real-time traffic conditions, so as to provide more efficient service for drivers. However, if the vehicle obtains the local traffic conditions that the driver is more concerned about from the remote server, it will consume a heavy bandwidth and incurs an increased response delay. Generating and broadcasting local traffic conditions through fog computation is a feasible method to reduce communication costs, and most existing traffic monitoring systems based on fog computation are implemented using Bilinear Pairing operations. In this paper, we propose LPTM scheme with Elliptic Curve Cryptosystem (ECC), an optimized Timed Efficient Stream Loss-tolerant Authentication (TESLA) protocol is adopted to achieve efficient and secure communication, and we also use an identity-based signature scheme with partial message recovery (PMR-IBS) to effectively shorten the length of the fog node broadcast message. Detailed security proofs show that the requirements of security and privacy are all achieved, and better simulation performance is presented in both computation and communication overhead.
... To mitigate the privacy risk associated with collection and analysis of location data, several location privacy protection frameworks have been proposed, including encryption, anonymization and obfuscation. Location encryption methods [7][8][9][10][11][12][13] often come with high computational and resource costs [14]. Once decrypted, the data are no longer private, though still safe, to those who have the authority to access and view the data. ...
... Eq (11) indicates that, in expectation, the squared distance between two sanitized GPS locations always deviates from the squared original distance by the same amount 12 −2 , regardless of d ij ; however, Eq (12) implies that the deviation is not meaningful for large d ij . ...
Preprint
The rapid growth of GPS technology and mobile devices has led to a massive accumulation of location data, bringing considerable benefits to individuals and society. One of the major usages of such data is travel time prediction, a typical service provided by GPS navigation devices and apps. Meanwhile, the constant collection and analysis of the individual location data also pose unprecedented privacy threats. We leverage the notion of geo-indistinguishability (GI), an extension of differential privacy to the location privacy setting, and propose a procedure for privacy-preserving travel time prediction without collecting actual individual GPS trace data. We propose new concepts to examine the impact of the GI sanitization on the usefulness of GPS traces, and provide analytical and experimental utility analysis of the privacy-preserving travel time reliability analysis. We also propose new metrics to measure the adversary error in learning individual GPS traces from the collected sanitized data. Our experiment results suggest that the proposed procedure provides travel time analysis with satisfactory accuracy at reasonably small privacy costs.
... Mobile Crowdsensing (MCS) is a technique where a large group of users utilising smart devices such as smart phones, wearables, tablets and in-vehicle sensing devices equipped with various sensors to share sensory data from surroundings [1]. With the motivation of potential benefits to collect data without additional costs, MCS is extensively researched by scholars and has various applications such as vehicles Crowdsensing paradigm [2], noise monitoring [3], extreme driving behaviors detecting [4] and so on. ...
... Besides, AI-based mitigation of fake tasks protects the majority of MCS players from the battery-oriented illegitimate task submissions. The impacted recruits and participants are impacted recruits = F T recruits T otal recruits (2) impacted participants = P articipants in F T T otal participants ...
Conference Paper
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... Basudan et al. [1] proposed a privacypreserving road surface condition monitoring system using fog computing and crowdsensing. Li et al. [10] proposed a privacy-preserving traffic monitoring scheme crowdsensing-based, which adopts short group signature to authenticate identities and uses range query algorithm to collect driving information, and filters out false reports fog-assisted through WiFi challenge handshake. Besides, Zhang et al. [41] proposed an efficient, verifiable and privacy-preserving traffic management system via mobile crowdsensing, which achieves traffic flows statistics at road intersections and utilizes homomorphic encryption technique and differential privacy mechanism to satisfy desirable security properties. ...
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... Location-sensitive and latency-aware local data management in a traffic monitoring system have been considered by Li et al in [20] to propose a privacy-preserving traffic monitoring scheme via fog-enhanced vehicular crowdsensing. In the proposed PSM scheme with false report filtering feature, real-time traffic monitoring which supports data confidentiality and integrity, conditional privacy, and local traffic processing is achieved. ...
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... Through considering synthetically with information confidentiality, mutual authenticity, integrity, privacy, and anonymity, the authors in [31] enhanced security in data transmission of vehicular crowdsensing-based road surface condition monitoring system. Li et al. [32] addressed the security threats in fog-assisted vehicular crowdsensing system. The security aspects included driver authentication, The collected data of road map updating ...
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... Due to the wide distribution and high mobility, taxis have great applications for mobile crowd sensing (MCS) [4,5]. Many urban sensing tasks (e.g., traffic monitoring [10], accident detection [20], road surface sensing [21], and crowdsourced logistics [3]) rely on citywide spatio-temporal data collected by taxis. To perceive human mobility and facilitate task allocation [30], it is necessary to estimate the taxi passenger Chao Chen ivanchao.chen@gmail.com ...
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