Crowdsourcing platforms offer the unprecedented opportunity to connect easily on-demand task providers, or requesters, and on-demand task solvers, or workers, locally or world-wide, for paid or voluntary work, and for various kinds of tasks. By facilitating the accurate search of specific workers, otherwise unavailable, they have the potential to reduce costs as well as to accelerate and even democratize innovation. However, abusive behaviors from crowdsourcing platforms against requesters or workers are frequently reported in the news or on dedicated websites, whether performed willingly or not, putting them at the epicenter of a burning societal debate.
The goal of the Crowdguard project is to design sound protection measures of the requesters and workers from threats coming from the platform, while still enabling the latter to perform efficient and accurate tasks assignments. We will address these challenges by :
* Proposing secure distributed algorithms for allowing workers (resp. requesters) to collaboratively compute a privacy-preserving version of their profiles (resp. a confidentiality-preserving version of their tasks) which will then be sent to the platform. We plan to intertwin encryption with differential privacy in order to reconcile privacy with efficiency.
* Identifying and formalizing the possible abusive behaviors that the platform may perform, and extending the previously defined privacy-preserving models/algorithms in order to prevent them.