Data Leak Aware Crowdsourcing in Social Network


Harnessing human computation for solving complex problems call spawns the issue of finding the unknown competitive group of solvers. In this paper, we propose an approach called Friendlysourcing to build up teams from social network answering a business call, all the while avoiding partial solution disclosure to competitive groups. The contributions of this paper include (i) a clustering based approach for discovering collaborative and competitive team in social network (ii) a Markov-chain based algorithm for discovering implicit interactions in the social network.
Keywords: Social network, outsourcing human-computation,privacy


Data Leak Aware Crowdsourcing in Social Network
  • décembre 9, 2012
  • Université Paris Sorbone Cité, Paris Descartes, France
Friendlysourcing: Data Disclosure Aware Discovering Collaborative and Competitive Clusters in Social Network
  • mai 1, 2013
  • Université Paris Descartes, France RMIT University, Australia
Be a Collaborator and a Competitor in Crowdsourcing System
  • avril 29, 2014
  • Université Paris Sorbone Cité Paris Descartes France, Freie Universitat Berlin Germany
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