Abstract: Crowdsourcing is an emerging alternative of outsourcing strategy that aims at making use of the wisdom of the crowd.Dueto the cheap and efficient characteristics of crowdsourcing,it’s widely recognized as an ideal solution for massive data oriented processing tasks,such as data labeling and model training.In crowdsourcing,however,on the task owners side,to get benifits from the wisdom of the unforeseen workers,they have to first make their private data unlimited accessed publicly,which is unsafe as the risk of the information leakage is concerned.To address this issue,we propose a crowdsourcing model PrivCS that can ensure content privacy security.The essential idea of PrivCS is to synthetiz some new data with regard to the task owners’ private data and pulicly publish the synthetic data to the workers instead of the real data.The tool we adopt to synthetiz the new data is the adversarial generative networks(GAN).There have been lots of exploitations show that GAN is privacy-preserving,therefore PrivCS of course inherits the same ability from GAN.We also study the theoretic performance of PrivCS,our analysis show that the outputs of PrivCS are comparable with respect to those derived from the real data,in terms of both data labeling and model training tasks.In addition,our experimental results support the theoretic findings.

Key words: Crowdsourcing, Privacy preserving, Generating adversarial networks