A privacy-preserving sensory data sharing scheme in Internet of Vehicles

Kong, Qinglei; Lu, Rongxing; Ma, Maode; Bao, Haiyong

doi:10.1016/j.future.2017.12.003

Cited by 71 publications

(59 citation statements)

References 26 publications

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“…However, their approach did not consider the problem of protecting information security or reliable transmission of incident flow. e approaches in [19][20][21][22] achieve a reasonable level of privacy protection. In these studies, vehicles are issued pseudonym certificates, and certificate generation and provisioning are divided among multiple organisations.…”

Section: Related Workmentioning

confidence: 99%

“…Meanwhile, in Figure 15, we plot and compare the computation cost of RSU in the three schemes in terms of the data dimension that ranges from 5 to 40, during the data acquisition phase. e comparison results show that compared with the [22] scheme and traditional scheme, STEIM can greatly reduce the computation complexity of the involved entities.…”

Section: Performance Analysismentioning

confidence: 99%

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STEIM: A Spatiotemporal Event Interaction Model in V2X Systems Based on a Time Period and a Raster Map

Luo

Bao

et al. 2020

Mobile Information Systems

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e Internet of Vehicles (IoV) is an important artificial intelligence research field for intelligent transportation applications. Complex event interactions are important methods for data flow processing in a Vehicle to Everything (V2X) environment. Unlike the classic Internet of ings (IoT) systems, data streams in V2X include both temporal information and spatial information. us, effectively expressing and addressing spatiotemporal data interactions in the IoV is an urgent problem. To solve this problem, we propose a spatiotemporal event interaction model (STEIM). STEIM uses a time period and a raster map for its temporal model and spatial model, respectively. In this paper, first, we provide a spatiotemporal operator and a complete STEIM grammar that effectively expresses the spatiotemporal information of the spatiotemporal event flow in the V2X environment. Second, we describe the design of the operational semantics of the STEIM from the formal semantics. In addition, we provide a spatiotemporal event-stream processing algorithm that is based on the Petri net model. e STEIM establishes a mechanism for V2X event-stream temporal and spatial processing. Finally, the effectiveness of the STEIM-based system is demonstrated experimentally.

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Section: Related Workmentioning

confidence: 99%

Section: Performance Analysismentioning

confidence: 99%

STEIM: A Spatiotemporal Event Interaction Model in V2X Systems Based on a Time Period and a Raster Map

Luo

Bao

et al. 2020

Mobile Information Systems

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“…Some works closely related to this paper are briefly reviewed below. In F-VSNs, massive sensory data is produced in each data dimension, and needs to be uploaded for further processing and analysis; data aggregation schemes [16][17][18][19][20][21][22][23] have received considerable attention recently, and are roughly classified into two categories: single-dimensional data aggregation [16][17][18][19] and multi-dimensional data aggregation [20][21][22][23]. Zhuo et al [16] introduced a data aggregation scheme, which protects each involved entity's identity privacy, and allows the requester to examine the correctness of the obtained results.…”

Section: Related Workmentioning

confidence: 99%

“…However, using the existing data aggregation schemes [16][17][18][19][20][21][22] cannot determine the number of data reports produced in each road segment, and cannot compute the average sensory data in each road segment. To solve the problem, the scheme [23] exploits the Chinese remainder theorem and Paillier cryptosystem to calculate the average sensory data in each segments; however, it brings heavy computation and communication costs. In addition, to choose an optimal route, vehicles often query about the road conditions of the potential moving routes, but the query reports uploaded by vehicles are tightly associated with the query location, and thus the query location could be disclosed.…”

Section: Introductionmentioning

confidence: 99%

“…The oblivious transfer [24,25], homomorphic encryption technology [26,27], and proxy re-encryption technique [23] have been exploited to hide the query location. However, it is worth noting that the computation and communication costs by the schemes [24,25] is directly proportional to the data dimension, the schemes [26,27] do not support the scenario with high vehicle density, and the scheme [23] needs heavy computation and communication costs.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Privacy-Preserving Data Sharing for Fog-Assisted Vehicular Sensor Networks

Yang

2020

Sensors

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Vehicular sensor networks (VSNs) have emerged as a paradigm for improving traffic safety in urban cities. However, there are still several issues with VSNs. Vehicles equipped with sensing devices usually upload large amounts of data reports to a remote cloud center for processing and analyzing, causing heavy computation and communication costs. Additionally, to choose an optimal route, it is required for vehicles to query the remote cloud center to obtain road conditions of the potential moving route, leading to an increased communication delay and leakage of location privacy. To solve these problems, this paper proposes an efficient privacy-preserving data sharing (EP 2 DS) scheme for fog-assisted vehicular sensor networks. Specifically, the proposed scheme utilizes fog computing to provide local data sharing with low latency; furthermore, it exploits a super-increasing sequence to format the sensing data of different road segments into one report, thus saving on the resources of communication and computation. In addition, using the modified oblivious transfer technology, the proposed scheme can query the road conditions of the potential moving route without disclosing the query location. Finally, an analysis of security suggests that the proposed scheme can satisfy all the requirements for security and privacy, with the evaluation results indicating that the proposed scheme leads to low costs in computation and communication.

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A verifiable and privacy‐preserving multidimensional data aggregation scheme in mobile crowdsensing

Jiang

Zhao

Tang

et al. 2020

Trans Emerging Tel Tech

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Mobile crowdsensing has become a popular data collection paradigm and has been extensively studied. Since the analysis of sensory data usually reveals privacy of mobile users, data aggregation technology is widely used to avoid privacy disclosure. However, traditional privacy‐preserving data aggregation schemes cannot provide aggregate statistics of multidimensional data in fine‐grained areas or resist collusive attacks. Moreover, the cloud platform is not fully trusted, and it is challenging to verify the correctness of data aggregation results. To provide privacy‐preserving multidimensional statistics within fine‐grained areas and verify results of data aggregation, we propose a verifiable privacy‐preserving data aggregation scheme in this article, which is based on data double‐masking, Shamir's secret sharing, and bilinear mapping. Specifically, a key agreement is utilized to establish mask seeds for users, while the users are required to extend mask values and generate the verification key with bilinear mapping. The cloud platform aggregates masked data and provides proof of computational results. Theoretical analysis demonstrates our scheme can protect data and location privacy, verify the aggregation results, and improve the rubustness to failure. The simulation results show that the computational overhead of mobile devices by using the proposed anticollusion scheme is better than that of homomorphic encryption. In addition, the computational complexity of task requesters is less than that of the PVPA scheme for verification of multidimensional aggregation results.

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A privacy-preserving sensory data sharing scheme in Internet of Vehicles

Cited by 71 publications

References 26 publications

STEIM: A Spatiotemporal Event Interaction Model in V2X Systems Based on a Time Period and a Raster Map

STEIM: A Spatiotemporal Event Interaction Model in V2X Systems Based on a Time Period and a Raster Map

Efficient Privacy-Preserving Data Sharing for Fog-Assisted Vehicular Sensor Networks

A verifiable and privacy‐preserving multidimensional data aggregation scheme in mobile crowdsensing

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