A blockchain-based location privacy-preserving crowdsensing system

Yang, Mengmeng; Zhu, Tianqing; Liang, Kaitai; Zhou, Wanlei; Deng, Robert H.

doi:10.1016/j.future.2018.11.046

Cited by 154 publications

(79 citation statements)

References 19 publications

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“…[34] proposed a crowdsensing quality control model based on blockchain, designing the two-consensus process to achieve a means to evaluate data quality. [35][36][37] considered the privacy issue by utilizing blockchain. [35] proposed a cooperation verification approach to protect the k-anonymity privacy.…”

Section: Blockchain Backgroundmentioning

confidence: 99%

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A Blockchain-Based Hybrid Incentive Model for Crowdsensing

Wei

Long

2020

Electronics

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Crowdsensing is an emerging paradigm of data aggregation, which has a pivotal role in data-driven applications. By leveraging the recruitment, a crowdsensing system collects a large amount of data from mobile devices at a low cost. The critical issues in the development of crowdsensing are platform security, privacy protection, and incentive. However, the existing centralized, platform-based approaches suffer from the single point of failure which may result in data leakage. Besides, few previous studies have addressed the considerations of both the economic incentive and data quality. In this paper, we propose a decentralized crowdsensing architecture based on blockchain technology which will help improve the attack resistance. Furthermore, we present a hybrid incentive mechanism, which integrates the data quality, reputation, and monetary factors to encourage participants to contribute their sensing data while discouraging malicious behaviors. The effectiveness our of proposed incentive model is verified through a combination of the theory of mechanism design. The performance analysis and simulation results illustrate that the proposed hybrid incentive model is a reliable and efficient mean to promote data security and incentivizing positive conduct on the crowdsensing application.

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Section: Blockchain Backgroundmentioning

confidence: 99%

“…[35] proposed a cooperation verification approach to protect the k-anonymity privacy. [36] constructed the private blockchain network to prevent the identity-based attacks. [37] aimed to handle the location privacy protection by designing the confusion mechanism based on an information coding method.…”

Section: Blockchain Backgroundmentioning

confidence: 99%

A Blockchain-Based Hybrid Incentive Model for Crowdsensing

Wei

Long

2020

Electronics

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“…Usually, the server platform is a centralized corporation or organization with strong capacities in storage and computation. There are also some designs that adopt decentralized structure by employing blockchain technique [75].…”

Section: ) System Entitiesmentioning

confidence: 99%

Privacy protection in mobile crowd sensing: a survey

et al. 2019

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The unprecedented proliferation of mobile smart devices has propelled a promising computing paradigm, Mobile Crowd Sensing (MCS), where people share surrounding insight or personal data with others. As a fast, easy, and cost-effective way to address large-scale societal problems, MCS is widely applied into many fields, e.g., environment monitoring, map construction, public safety, etc. Despite the popularity, the risk of sensitive information disclosure in MCS poses a serious threat to the participants and limits its further development in privacy-sensitive fields. Thus, the research on privacy protection in MCS becomes important and urgent. This paper targets the privacy issues of MCS and conducts a comprehensive literature research on it by providing a thorough survey. We first introduce a typical system structure of MCS, summarize its characteristics, propose essential requirements on privacy on the basis of a threat model. Then, we survey existing solutions on privacy protection and evaluate their performances by employing the proposed requirements. In essence, we classify the privacy protection schemes into four categories with regard to identity privacy, data privacy, attribute privacy, and task privacy. Besides, we review the achievements on privacy-preserving incentives in MCS from four viewpoints of incentive measures: credit incentive, auction incentive, currency incentive, and reputation incentive. Finally, we point out some open issues and propose future research directions based on the findings from our survey.

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“…Yun Wang et al proposed a privacy-preserving framework for signature-based intrusion detection in a distributed network based on fog devices. 25 To prevent breaches of privacy, Yang et al 26 proposed a novel blockchain-based privacy-preserving crowdsensing system. They use the anonymous nature of blockchains to protect the real identity of workers.…”

Section: Existing Research Work and Their Shortcomingsmentioning

confidence: 99%

Energy-efficient and privacy-preserving spatial range aggregation query processing in wireless sensor networks

Liu

Wang

2019

International Journal of Distributed Sensor Networks

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The existing privacy-preserving aggregation query processing methods in sensor networks rely on pre-established network topology and require all nodes in the network to participate in query processing. Maintaining the topology results in a large amount of energy overhead, and in many cases, the user is interested only in the aggregated query results of some areas in the network, and thus, the participation of the entire network node is not necessary. Aiming to solve this problem, this article proposes a spatial range aggregation query algorithm for a dynamic sensor network with privacy protection (energy-efficient privacy-preserving data aggregation). The algorithm does not rely on the pre-established topology but considers only the query area that the user is interested in, abandoning all nodes to participate in distributing the query messages while gathering the sensory data in the query range. To protect node data privacy, Shamir’s secret sharing technology is used to prevent internal attackers from stealing the sensitive data of the surrounding nodes. The analysis and experimental results show that the proposed algorithm outperforms the existing algorithms in terms of energy and privacy protection.

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A blockchain-based location privacy-preserving crowdsensing system

Cited by 154 publications

References 19 publications

A Blockchain-Based Hybrid Incentive Model for Crowdsensing

A Blockchain-Based Hybrid Incentive Model for Crowdsensing

Privacy protection in mobile crowd sensing: a survey

Energy-efficient and privacy-preserving spatial range aggregation query processing in wireless sensor networks

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