Privacy-Preserving Incentive Mechanisms for Truthful Data Quality in Data Crowdsourcing

Data crowdsourcing is a promising paradigm that leverages the "wisdom" of a potentially large crowd of "workers" in many application domains. Quality-aware crowdsourcing is beneficial as it makes use of workers' data quality to perform task allocation and data aggregation. However, a worker's quality and data can be her private information that she may have incentive to misreport to the crowdsourcing requester. Moreover, a worker's quality and data can depend on her sensitive information (e.g., location), which can be inferred from the outcomes of task allocation and data aggregation by an adversary. In this paper, we devise Privacy-preserving crowdsourcing mechanisms for truthful Data Quality Elicitation (PDQE). In these mechanisms, we design differentially private task allocation and data aggregation algorithms to prevent the inference of a worker's quality and data from the outcomes of these algorithms. In the meantime, the mechanisms also incentivize workers to truthfully report their quality and data and make desired efforts. We first focus on the mechanisms for a single task (S-PDQE) and then extend it to the case of multiple tasks (M-PDQE). We further show that both the mechanisms achieve a bounded performance gap compared to the optimal strategy. We evaluate the proposed mechanisms using simulations based on real-world data, which corroborate their highly-desired properties on truthful data quality elicitation, data accuracy and privacy protection.