Anonymity-Based Privacy-Preserving Data Reporting for Participatory Sensing

Yao, Yonglei; Yang, Laurence T.; Xiong, Neal N.

doi:10.1109/jiot.2015.2410425

Cited by 35 publications

(21 citation statements)

References 22 publications

(28 reference statements)

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“…Our analysis has shown that most studied proposals have addressed energy efficiency issues, while only some of these have introduced mechanisms to mitigate security and privacy concerns. In fact, we find that very few solutions [6,19,24,53,66] actually account for both privacy and energy efficiency issues. We now proceed to discuss these prominent solutions in more detail.…”

Section: Privacy and Energymentioning

confidence: 96%

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A Survey on Smartphone-Based Crowdsensing Solutions

Zamora

Calafate

Cano

et al. 2016

Mobile Information Systems

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In recent years, the widespread adoption of mobile phones, combined with the ever-increasing number of sensors that smartphones are equipped with, greatly simplified the generalized adoption of crowdsensing solutions by reducing hardware requirements and costs to a minimum. These factors have led to an outstanding growth of crowdsensing proposals from both academia and industry. In this paper, we provide a survey of smartphone-based crowdsensing solutions that have emerged in the past few years, focusing on 64 works published in top-ranked journals and conferences. To properly analyze these previous works, we first define a reference framework based on how we classify the different proposals under study. The results of our survey evidence that there is still much heterogeneity in terms of technologies adopted and deployment approaches, although modular designs at both client and server elements seem to be dominant. Also, the preferred client platform is Android, while server platforms are typically web-based, and client-server communications mostly rely on XML or JSON over HTTP. The main detected pitfall concerns the performance evaluation of the different proposals, which typically fail to make a scalability analysis despite being critical issue when targeting very large communities of users.

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Section: Privacy and Energymentioning

confidence: 96%

“…The last proposal in this group is Anonymity [66], which proposes an anonymous data reporting protocol for participatory applications. The idea is that the protocol avoids including identification information that can be vulnerable.…”

Section: Privacy and Energymentioning

confidence: 99%

A Survey on Smartphone-Based Crowdsensing Solutions

Zamora

Calafate

Cano

et al. 2016

Mobile Information Systems

View full text Add to dashboard Cite

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“…Similarly, Yao et al proposed an anonymous data reporting protocol [76]. The protocol includes two stages: slot reservation and message submission.…”

Section: Identity Privacy Preservation (Id)mentioning

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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“…Some studies adopt the idea of protecting the collector data from both other collectors and the data requesters responsible for the crowdsensing platform. Yao et al [185] proposed a anonymity-based privacy-preserving data reporting protocol. They broke the link between any data and the participant who reports the data.…”

Section: A Rq1: What Is the Strategy Used To Ensure The Data Credibimentioning

confidence: 99%

Credibility on Crowdsensing Data Acquisition

Neto

Gomes

Soares

2019

JCIS

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This paper focuses on the credibility of crowd sensed data. The ubiquity of crowdsensing platforms has enabled the capture of sensed information useful for several applications domains. However, one concern with crowdsensing is the information credibility and, over the last few years, we have seen a variety of approaches to leverage credibility on the crowdsensing platforms. Here, we carried out a systematic mapping study on 177 credibility-related articles about crowdsensing. The results show that the absence of standard models in the data capture process and the human factors such as individualism, inattention, and the possibility of errors (whether they are intentional or not), are the primary harmful to the credibility on these platforms. Additionally, we propose a three-level taxonomy that classifies crowdsensing credibility into two broad branches: direct and indirect approaches. To the best of our knowledge, we believe this is the first systematic mapping to address both credibility approaches and the factors that negatively impact the data credibility on crowdsensing platforms.

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Anonymity-Based Privacy-Preserving Data Reporting for Participatory Sensing

Cited by 35 publications

References 22 publications

A Survey on Smartphone-Based Crowdsensing Solutions

A Survey on Smartphone-Based Crowdsensing Solutions

Privacy protection in mobile crowd sensing: a survey

Credibility on Crowdsensing Data Acquisition

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