Abstract

Crowdsourcing is the practice of allowing companies to use human intelligence scale to provide solutions to issues they want to outsource. Outsourced issues are increasingly complex and cannot be resolved individually. We propose in this thesis an approach called SocialCrowd, helping to improve the quality of the results of crowdsourcing. It compromise to collaborate participants to unite solving ability and provide solutions to outsourced problems more and more complex. Collaborative groups are put in competition through attractive remuneration, in order to obtain better resolution. Furthermore, it is necessary to protect the private information of competing groups. We use social media as a support for data leakage. We propose an approach based on Dijkstra algorithm to estimate the propagation probability of private data member in the social network. Given the size of social networks, this computation is complex. Parallelization of computing is proposed according to the MapReduce model. A classification algorithm based on the calculation of propagations in social networks is proposed for grouping participants in collaborative and competitive groups while minimizing data leaks from one group to another. As this classification problem is a combinatorial complexity, we proposed a classification algorithm based on combinatorial optimization algorithms such as simulated annealing and genetic algorithms. Given the large number of feasible solutions, an approach based on the model of Soft Constraint Satisfaction Problem (SCSP) is proposed to classify the different solutions.