Collusion-resistant Spatial Phenomena Crowdsourcing via Mixture of Gaussian Processes Regression

With the rapid development of mobile devices, spatial location-based crowdsourcing applications have attracted much attention. These applications also introduce new security risks due to untrustworthy data sources. In the context of crowdsourcing applications for spatial interpolation (i.e. spatial regression) using crowdsourced data, the results can be seriously affected if malicious data sources initiate a colluding (collaborate) attacks which purposely alter some of the measurements. To combat this serious detrimental effect, and to mitigate such attacks, we develop a robust version via a Gaussian Process mixture model and develop a computationally efficient algorithm which utilises a Markov chain Monte Carlo (MCMC)-based methodology to produce an accurate predictive inference in the presence of collusion attacks. The algorithm is fully Bayesian and produces posterior predictive distribution for any point-of-interest in the input space. It also assesses the trustworthiness of each worker, i.e. the probability of each worker being honest (trustworthy). Simulation results demonstrate the accuracy of this algorithm.