Lightweight Privacy-Preserving Task Assignment in Skill-Aware Crowdsourcing

  • Louis Béziaud
  • Tristan Allard
  • David Gross-Amblard
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10439)

Abstract

Crowdsourcing platforms dedicated to work are used by a growing number of individuals and organizations, for tasks that are more and more diverse, complex, and that require very specific skills. These highly detailed worker profiles enable high-quality task assignments but may disclose a large amount of personal information to the central platform (e.g., personal preferences, availabilities, wealth, occupations), jeopardizing the privacy of workers. In this paper, we propose a lightweight approach to protect workers privacy against the platform along the current crowdsourcing task assignment process. Our approach (1) satisfies differential privacy by letting each worker perturb locally her profile before sending it to the platform, and (2) copes with the resulting perturbation by leveraging a taxonomy defined on workers profiles. We overview this approach below, explaining the lightweight upgrades to be brought to the participants. We have also shown (full version of this paper [1]) formally that our approach satisfies differential privacy, and empirically, through experiments performed on various synthetic datasets, that it is a promising research track for coping with realistic cost and quality requirements.

Keywords

Crowdsourcing Task assignment Differential privacy Randomized response 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Louis Béziaud
    • 1
    • 3
  • Tristan Allard
    • 1
    • 2
  • David Gross-Amblard
    • 1
    • 2
  1. 1.Univ. Rennes 1RennesFrance
  2. 2.IRISARennesFrance
  3. 3.ENS RennesRennesFrance

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