Lightweight privacy-preserving truth discovery for vehicular air quality monitoring

Pan, Jianping

doi:10.1016/j.dcan.2022.03.021

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…Liu and Pan [19] presented a data masking-based privacy-preserving truth discovery framework that incorporates spatial and temporal correlations to solve the sparsity problem. The framework makes it possible to monitor air quality at a fine granularity using vehicular crowd sensing systems.…”

Section: Related Workmentioning

confidence: 99%

Truth Discovery Technology for Mobile Crowd Sensing in Water Quality Monitoring

Chen

Mao

2023

Wireless Communications and Mobile Computing

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The water quality of urban inland rivers is an important index of urban environmental health, which can reflect a city’s development level and its social and economic development. The water quality of these rivers strongly impacts the health and quality of life of the residents of urban and surrounding areas. Therefore, it is necessary to accurately assess the quality of water in urban inland rivers, which can also aid environmental protection departments in providing river governance. Generally, the water quality status of a city’s inland rivers is assessed and released by environmental monitoring stations in various regions that deploy the corresponding water quality detection equipment at certain major locations of the river. However, these detection devices can only detect water quality at fixed locations, and often, the water quality of an urban inland river changes owing to the impact of the surrounding environment and residents it serves. Therefore, the water quality around a detection point does not always reflect the water quality of the entire river section. To better express the water quality status of a city’s inland river, we propose a method based on a mobile crowd-sensing system that obtains the water quality data of the river during an entire period of time and then fuzes these sensing data to obtain the best truth-value estimate of the water quality of the river. We can use this water quality truth value to conduct an objective evaluation of the water quality of a city’s inland rivers. The water quality parameters obtained by the method can better represent the water quality status of the river, and the data are more accurate compared to the data collected and released by an environmental monitoring station. Through simulation and comparative analysis, we found that the water quality data obtained by the proposed method were more accurate, indicating that our method has more practical value than the detection device method.

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Section: Related Workmentioning

confidence: 99%

Truth Discovery Technology for Mobile Crowd Sensing in Water Quality Monitoring

Chen

Mao

2023

Wireless Communications and Mobile Computing

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“…The sustained attention is driven by ongoing technological advancements, which give rise to many potential scenarios and applications. For example, in smart city, traveling vehicles can monitor the air pollution or noise level at a fine granularity [3], [4]. With federated learning, vehicles can perform as local workers, contributing their local training results to build a globally shared learning model for parking space estimation [5].…”

Section: Introductionmentioning

confidence: 99%

CRS: A Privacy-Preserving Two-Layered Distributed Machine Learning Framework for IoV

Pan

2024

IEEE Internet Things J.

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Secure and privacy-preserving data aggregation in the Internet of Vehicles (IoV) continues to be a focal point of interest in both the industry and academia. Aiming at tackling the challenges and solving the remaining limitations of existing works, this paper introduces a novel Schnorr approvalbased IoV data aggregation framework based on a two-layered architecture. In this framework, a server can aggregate the IoV data from clusters without inferring the raw data, real identity and trajectories of vehicles. Notably, we avoid incorporating the widely-accepted techniques such as homomorphic encryption and digital pseudonym to avoid introducing high computation cost to vehicles. We propose a novel concept, data approval, based on the Schnorr signature scheme. With the approval, the fake data injection attack carried out by a cluster head can be defended against. The separation of liability is achieved as well. The evaluation shows that the framework is secure and lightweight for vehicles in terms of the computation and communication costs.

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