General Privacy-Preserving Verifiable Incentive Mechanism for Crowdsourcing Markets
Jiajun Sun

TL;DR
This paper introduces a privacy-preserving, verifiable incentive mechanism for crowdsourcing markets that protects user privacy and ensures correct payments, matching the effectiveness of non-privacy-preserving methods.
Contribution
It proposes a novel incentive mechanism that guarantees privacy and verifiability in crowdsourcing markets with budget constraints, extending prior work by addressing security and privacy challenges.
Findings
Achieves privacy protection for bids, assignments, and identities.
Ensures verifiable payments between platform and users.
Maintains same results as non-privacy-preserving mechanisms.
Abstract
In crowdsourcing markets, there are two different type jobs, i.e. homogeneous jobs and heterogeneous jobs, which need to be allocated to workers. Incentive mechanisms are essential to attract extensive user participating for achieving good service quality, especially under a given budget constraint condition. To this end, recently, Singer et al. propose a novel class of auction mechanisms for determining near-optimal prices of tasks for crowdsourcing markets constrained by the given budget. Their mechanisms are very useful to motivate extensive user to truthfully participate in crowdsourcing markets. Although they are so important, there still exist many security and privacy challenges in real-life environments. In this paper, we present a general privacy-preserving verifiable incentive mechanism for crowdsourcing markets with the budget constraint, not only to exploit how to protect…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Mobile Crowdsensing and Crowdsourcing · Blockchain Technology Applications and Security
