Data Mining and Knowledge Discovery

, Volume 28, Issue 5–6, pp 1314–1335 | Cite as

Preserving worker privacy in crowdsourcing

Article

Abstract

This paper proposes a crowdsourcing quality control method with worker-privacy preservation. Crowdsourcing allows us to outsource tasks to a number of workers. The results of tasks obtained in crowdsourcing are often low-quality due to the difference in the degree of skill. Therefore, we need quality control methods to estimate reliable results from low-quality results. In this paper, we point out privacy problems of workers in crowdsourcing. Personal information of workers can be inferred from the results provided by each worker. To formulate and to address the privacy problems, we define a worker-private quality control problem, a variation of the quality control problem that preserves privacy of workers. We propose a worker-private latent class protocol where a requester can estimate the true results with worker privacy preserved. The key ideas are decentralization of computation and introduction of secure computation. We theoretically guarantee the security of the proposed protocol and experimentally examine the computational efficiency and accuracy.

Keywords

Crowdsourcing Quality control Privacy-preserving data mining  EM algorithm 

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Copyright information

© The Author(s) 2014

Authors and Affiliations

  1. 1.Department of Mathematical Informatics, Graduate School of Information Science and TechnologyThe University of TokyoTokyo Japan
  2. 2.Advanced Center for Computing and CommunicationRIKENSaitamaJapan
  3. 3.Department of Intelligence Science and Technology, Graduate School of InformaticsKyoto UniversitySakyo-kuJapan

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