On the Data Quality in Privacy-Preserving Mobile Crowdsensing Systems with Untruthful Reporting

Zhao, Cong; Yang, Shusen; McCann, Julie A.

doi:10.1109/tmc.2019.2943468

Cited by 36 publications

(7 citation statements)

References 36 publications

(86 reference statements)

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“…Huang et al [2] combined the advantage of differential privacy as well as the advantage of privacy preservation incentive allocation, and proposed a noise controlled scheme for privacy preservation incentive allocation scheme. Zhao et al [17] utilized the scheme of distributing credible incentive to further improve the quality of sensing result.…”

Section: Privacy Preservation For Corwdsensingmentioning

confidence: 99%

“…Ni et al [16] combined the issues of privacy preservation and the precision of task allocation, and then based on the strategy of proxy re-encryption they proposed a scheme with strong privacy preservation and precision task allocation for mobile crowdsensing. Considered the data quality of feeding back result, Zhao et al [17] proposed a privacy preservation scheme for untruthful feeding back result for mobile crowdsensing.…”

Section: Introductionmentioning

confidence: 99%

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A grid and consensus based privacy preservation scheme for crowdsensing

Zheng

Cui

et al. 2023

Preprint

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In the process of task allocation in crowdsensing, the precise location of the sensing initiator or the location of the sensing user may be released to each other, which will violate the privacy of various users in crowdsensing. Thus, in order to cope with the problem of privacy leakage in crowdsensing, and further deal with the problem of inconformity in feeding back results, in this paper, a privacy preservation scheme for crowdsensing which is based on the conception of division grids and distributed consensus (short for GDDCC) has been proposed. In this scheme, the region used to publish sensing tasks is divided into smaller girds with enough sensing users, and a sub-area with sensing grids is sent to all users in this region. Then sensing users in each grid unify their sensing results and send the consensus result as the precise result to the sensing initiator. As a result, during the process of crowdsensing, the sensing initiator does not know the precise location of the sensing user and the sensing user does not know the precise location of the sensing initiator, so the privacy of each entity in the crowdsensing is preserved. In addition, the idea of prefix membership verification is used to preserve the privacy of sensing users in the same grid during the process of result consensus. Then in the section of security analysis, the privacy security of each entity is analysed, and in the section of performance analysis, experimental results with elaborate reasons are given to further demonstrate the superiority of the proposed GDDCC.

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Section: Privacy Preservation For Corwdsensingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A grid and consensus based privacy preservation scheme for crowdsensing

Zheng

Cui

et al. 2023

Preprint

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show abstract

“…Nonetheless, it is important to note that the user involvement in location-based tasks often raises concerns on location privacy: indeed, the higher the precision on device location, the higher the risk of location privacy leaks. Techniques such as spatial cloaking, differential geo-obfuscation, and blockchain are often included in MCS TA to preserve location privacy [5][74][75] [76]. Nevertheless, such techniques tend to reduce the QoI.…”

Section: A Challenges Typically Addressed By Task Allocation Approachesmentioning

confidence: 99%

Task Allocation Among Connected Devices: Requirements, Approaches, and Challenges

Pilloni

Ning

Atzori

2022

IEEE Internet Things J.

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Task allocation (TA) is essential when deploying application tasks to systems of connected devices with dissimilar and time-varying characteristics. The challenge of an efficient TA is to assign the tasks to the best devices, according to the context and task requirements. The main purpose of this paper is to study the different connotations of the concept of TA efficiency, and the key factors that most impact on it, so that relevant design guidelines can be defined. The paper first analyzes the domains of connected devices where TA has an important role, which brings to this classification: Internet of Things (IoT), Sensor and Actuator Networks (SAN), Multi-Robot Systems (MRS), Mobile Crowdsensing (MCS), and Unmanned Aerial Vehicles (UAV). The paper then demonstrates that the impact of the key factors on the domains actually affects the design choices of the state-ofthe-art TA solutions. It results that resource management has most significantly driven the design of TA algorithms in all domains, especially IoT and SAN. The fulfillment of coverage requirements is important for the definition of TA solutions in MCS and UAV. Quality of Information requirements are mostly included in MCS TA strategies, similar to the design of appropriate incentives. The paper also discusses the issues that need to be addressed by future research activities, i.e.: allowing interoperability of platforms in the implementation of TA functionalities; introducing appropriate trust evaluation algorithms; extending the list of tasks performed by objects; designing TA strategies where network service providers have a role in TA functionalities' provisioning.

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“…The challenge is then for the crowdsensing system to get the best out of the contributed data. Part of the solution lies in the elicitation of application-specific data analyses to reduce the loss of accuracy [22]. However, we argue that it is as important to foster the re-use of data across tasks, as also advocated by the IoT data marketplace trend [23].…”

Section: Introductionmentioning

confidence: 96%

Consent-driven data use in crowdsensing platforms: When data reuse meets privacy-preservation

Brahem

Scerri

Anciaux

et al. 2021

2021 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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Crowdsensing is an essential element of the IoT; it allows gathering massive data across time and space to feed our environmental knowledge, and to link such knowledge to user behavior. However, there are major obstacles to crowdsensing, including the preservation of privacy. The consideration of privacy in crowdsensing systems has led to two main approaches, sometimes combined, which are, respectively, to trade privacy for rewards, and to take advantage of privacy-enhancing technologies "anonymizing" the collected data. Although relevant, we claim that these approaches do not sufficiently take into account the users' own tolerance to the use of the data provided, so that the crowdsensing system guarantees users the expected level of confidentiality as well as fosters the use of crowdsensing data for different tasks. To this end, we introduce the -completeness property, which ensures that the data provided can be used for all the tasks to which their owners consent as long as they are analyzed with − 1 other sources, and that no privacy violations can occur due to the related contribution of users with less stringent privacy requirements. The challenge, therefore, is to ensure -completeness when analyzing the data while allowing the data to be used for as many tasks as possible and promoting the accuracy of the resulting knowledge. We address this challenge with a clustering algorithm sensitive to the data distribution, which is shown to optimize data reuse and utility using a dataset from a deployed crowdsensing application.

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On the Data Quality in Privacy-Preserving Mobile Crowdsensing Systems with Untruthful Reporting

Cited by 36 publications

References 36 publications

A grid and consensus based privacy preservation scheme for crowdsensing

A grid and consensus based privacy preservation scheme for crowdsensing

Task Allocation Among Connected Devices: Requirements, Approaches, and Challenges

Consent-driven data use in crowdsensing platforms: When data reuse meets privacy-preservation

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