Lightweight Privacy-Preserving Task Assignment in Skill-Aware Crowdsourcing

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


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.


Crowdsourcing Task assignment Differential privacy Randomized response 


  1. 1.
    Béziaud, L., Allard, T., Gross-Amblard, D.: Lightweight Privacy-Preserving Task Assignment in Skill-Aware Crowdsourcing (Full Version) (2017).
  2. 2.
    Bienaymé, I.-J.: Considérations à l’appui de la découverte de Laplace sur la loi de probabilité dans la méthode des moindres carrés. Mallet-Bachelier, Imprim (1853)Google Scholar
  3. 3.
    Blair, G., Imai, K., Zhou, Y.-Y.: Design and analysis of the randomized response technique. J. Am. Stat. Assoc. 110(511), 1304–1319 (2015)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006). doi: 10.1007/11787006_1 CrossRefGoogle Scholar
  5. 5.
    Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9(3–4), 211–407 (2014)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Kajino, H.: Privacy-Preserving Crowdsourcing. Ph.D. thesis, University of Tokyo (2016)Google Scholar
  7. 7.
    Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Res. Logistics Q. 2(1–2), 83–97 (1955)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Lease, M., Hullman, J., Bigham, J.P., Bernstein, M.S., Kim, J., Lasecki, W., Bakhshi, S., Mitra, T., Miller, R.C.: Mechanical turk is not anonymous. SSRN Electron. J. (2013)Google Scholar
  9. 9.
    Mavridis, P., Gross-Amblard, D., Miklós, Z.: Using hierarchical skills for optimized task assignment in knowledge-intensive crowdsourcing. In: Proceedings of WWW 2016, pp. 843–853 (2016)Google Scholar
  10. 10.
    McSherry, F.: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In: Proceedings of ACM SIGMOD 2009, pp. 19–30 (2009)Google Scholar
  11. 11.
    Warner, S.L.: Randomized response: a survey technique for eliminating evasive answer bias. J. Am. Stat. Assoc. 60(309), 63–69 (1965)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

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

Personalised recommendations