A Privacy-Preserving Incentive Mechanism for Participatory Sensing Systems

Niu, Xiaoguang; Wang, Jiawei; Ye, Qiongzan; Zhang, Yihao

doi:10.1155/2018/2593537

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…EPPI ([12,31]) allocates rewards using token-based E-Cents—an exchangeable and untraceable unit bearer currency. What the authors refer to as a mix zone facilitates the anonymous exchange of E-Cents between participants in order to provide untraceability.…”

Section: Related Workmentioning

confidence: 99%

Privacy Aware Incentivization for Participatory Sensing

Connolly

Dusparić

Iosifidis

et al. 2019

Sensors

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Participatory sensing is a process whereby mobile device users (or participants) collect environmental data on behalf of a service provider who can then build a service based upon these data. To attract submissions of such data, the service provider will often need to incentivize potential participants by offering a reward. However, for the privacy conscious, the attractiveness of such rewards may be offset by the fact that the receipt of a reward requires users to either divulge their real identity or provide a traceable pseudonym. An incentivization mechanism must therefore facilitate data submission and rewarding in a way that does not violate participant privacy. This paper presents Privacy-Aware Incentivization (PAI), a decentralized peer-to-peer exchange platform that enables the following: (i) Anonymous, unlinkable and protected data submission; (ii) Adaptive, tunable and incentive-compatible reward computation; (iii) Anonymous and untraceable reward allocation and spending. PAI makes rewards allocated to a participant untraceable and unlinkable and incorporates an adaptive and tunable incentivization mechanism which ensures that real-time rewards reflect current environmental conditions and the importance of the data being sought. The allocation of rewards to data submissions only if they are truthful (i.e., incentive compatibility) is also facilitated in a privacy-preserving manner. The approach is evaluated using proofs and experiments.

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Section: Related Workmentioning

confidence: 99%

Privacy Aware Incentivization for Participatory Sensing

Connolly

Dusparić

Iosifidis

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Methods using anonymous reputation architectures (Androulaki, Choi, Bellovin & Malkin, 2008) or pseudonyms (Li & Cao, 2013) have also been proposed to solve privacy and incentive-related conflicts in PS. However, they are vulnerable to identity-based attacks (Niu, Wang, Ye & Zhang, 2018). In addition, the privacy-enhancing peer-to-peer reputation system proposed by Kinateder & Pearson (2003) was not well utilized because of a lack of trust between PS participants and data collectors.…”

Section: Privacy Concernsmentioning

confidence: 99%

Connecting Citizens and Housing Companies for Fine-grained Air-Quality Sensing

Gupta¹,

Degbelo²,

Pebesma³

2018

giforum

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The complex nature of air quality suggests the need for fine-scale air-quality monitoring in cities. With 1 in 8 deaths worldwide being associated with air pollution in 2012, communities have started partnering with academic institutions and with state and federal agencies to assess local air quality and address these concerns. Participatory sensing has recently become one popular method for collecting air-quality information. It offers the prospect of collecting data at finer levels of granularity, but is subject to at least two significant challenges: data gaps (due, e.g., to the lack of calibration, maintenance and replacement of sensors), and citizens' concerns regarding privacy). We argue that including housing companies as stakeholders in participatory sensing frameworks may be beneficial in overcoming these challenges. A survey of housing companies suggests that they are willing to participate in air-quality monitoring for cities. The ideas presented here are pertinent to the design of more robust and privacy-aware participatory sensing frameworks.

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Participative Sensing Challenges

Guarda

Augusto

Lopes

et al. 2021

Information and Knowledge in Internet of Things

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A Privacy-Preserving Incentive Mechanism for Participatory Sensing Systems

Cited by 3 publications

References 26 publications

Privacy Aware Incentivization for Participatory Sensing

Privacy Aware Incentivization for Participatory Sensing

Connecting Citizens and Housing Companies for Fine-grained Air-Quality Sensing

Participative Sensing Challenges

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