Privacy-preserving worker allocation in crowdsourcing

Crowdsourcing has been a prevalent way to obtain answers for tasks that need human intelligence. In general, a crowdsourcing platform is responsible for allocating workers to each received task, with high-quality workers in priority. However, the allocation results can in turn yield knowledge about workers’ quality. For example, those unallocated workers are supposed to be less-qualified. They can be upset if such information is known by the public, which is an invasion of their privacy. To alleviate such concerns, we study the privacy-preserving worker allocation problem in this paper, aiming to properly allocate the workers while protecting their privacy. We propose worker allocation methods with the property of differential privacy, which proceed by first computing weights for each potential allocation and then sampling according to the weights. The Markov Chain Monte Carlo-based method is shown in our experiments to improve over the trivial random allocation method by 18.9% in terms of worker quality on synthetic data. On the real data, it realizes differential privacy with less than 20% loss on quality even when ϵ=13\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon = \frac{1}{3}$$\end{document}.