Cryptology ePrint Archive: Report 2020/404

From Zebras to Tigers: Incentivizing participation in Crowd-sensing applications through fair and private Bitcoin rewards

Tassos Dimitriou

Abstract: In this work we develop a rewarding framework that can be used as a building block in crowd-sensing applications. Although a core requirement of such systems is user engagement, people may be reluctant to participate as sensitive information about them may be leaked or inferred from submitted data. Thus monetary incentives could help attract a large number of participants, thereby increasing not only the amount but also the quality of sensed data. Our first contribution in this work is to ensure that users can submit data and obtain Bitcoin payments in a privacy-preserving manner, preventing curious providers from linking the data or the payments back to the user. At the same time, we thwart malicious user behavior such as double-redeeming attempts where a user tries to obtain rewards for multiple submissions of the same data. More importantly, we ensure the fairness of the exchange in a completely trustless manner; by relying on the Blockchain, we eliminate the trust placed on third parties in traditional fair exchange protocols. Finally, our system is highly efficient as most of the protocol steps do not utilize the Blockchain network. When they do, we only rely on simple Bitcoin transactions as opposed to prior works that are based on the use of highly complex smart contracts.

Category / Keywords: applications / Crowd-sensing, Participatory sensing, Security and Privacy, Data reporting, Incentives, Rewarding mechanisms, zkSNARKs, Bitcoin, Blockchain

Date: received 9 Apr 2020

Contact author: tassos dimitriou at ieee org

Available format(s): PDF | BibTeX Citation

Version: 20200413:102804 (All versions of this report)

Short URL: ia.cr/2020/404


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