{"type":"ScholarlyArticle","title":"A Trust-based Mixture of Gaussian Processes Model for Reliable Regression in Participatory Sensing","authors":[{"name":"Qikun Xiang","slug":"qikun-xiang","given":"Qikun","family":"Xiang","affiliation":"Nanyang Technological University"},{"name":"Jie Zhang","slug":"jie-zhang","given":"Jie","family":"Zhang","affiliation":"Nanyang Technological University"},{"name":"Ido Nevat","slug":"ido-nevat","given":"Ido","family":"Nevat","affiliation":""},{"name":"Pengfei Zhang","slug":"pengfei-zhang","given":"Pengfei","family":"Zhang","affiliation":"University of Oxford"}],"abstract":"Data trustworthiness is a crucial issue in real-world participatory sensing applications. Without considering this issue, different types of worker misbehavior, especially the challenging collusion attacks, can result in biased and inaccurate estimation and decision making. We propose a novel trust-based mixture of Gaussian processes (GP) model for spatial regression to jointly detect such misbehavior and accurately estimate the spatial field. We develop a Markov chain Monte Carlo (MCMC)-based algorithm to efficiently perform Bayesian inference of the model. Experiments using two real-world datasets show the superior robustness of our model compared with existing approaches.","doi":"10.24963/ijcai.2017/540","url":"https://ijcai.org/proceedings/2017/540","pdf":"https://ijcai.org/proceedings/2017/0540.pdf","markdown":"https://ijcai.org/proceedings/2017/540.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/540","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":540,"pages":{"first":3866,"last":3872},"keywords":["Multidisciplinary Topics and Applications: AI&Security and Privacy","Agent-based and Multi-agent Systems: Trust and Reputation"],"videos":[],"references":[{"position":1,"raw":"[Celis et al., 2016] L Elisa Celis, Sai Praneeth Reddy, Ishaan Preet Singh, and Shailesh Vaya. 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