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...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2019
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/87328 http://hdl.handle.net/10220/49471 http://ceur-ws.org/Vol-1578/ |
| _version_ | 1811687439381561344 |
|---|---|
| author | Zhang, Jie Xiang, Qikun Nevat, Ido Zhang, Pengfei |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Zhang, Jie Xiang, Qikun Nevat, Ido Zhang, Pengfei |
| author_sort | Zhang, Jie |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T05:16:20Z |
| format | Conference Paper |
| id | ntu-10356/87328 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:16:20Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/873282019-12-06T16:39:36Z Collusion-resistant spatial phenomena crowdsourcing via mixture of Gaussian Processes regression Zhang, Jie Xiang, Qikun Nevat, Ido Zhang, Pengfei School of Computer Science and Engineering 18th International Workshop on Trust in Agent Societies Crowdsourcing Gaussian DRNTU::Engineering::Computer science and engineering 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. Published version 2019-07-26T01:12:14Z 2019-12-06T16:39:36Z 2019-07-26T01:12:14Z 2019-12-06T16:39:36Z 2016 Conference Paper Xiang, Q., Nevat, I., Zhang, P. & Zhang, J. (2016). Collusion-resistant spatial phenomena crowdsourcing via mixture of Gaussian Processes regression. 18th International Workshop on Trust in Agent Societies, 1578, 19-30. https://hdl.handle.net/10356/87328 http://hdl.handle.net/10220/49471 http://ceur-ws.org/Vol-1578/ en © 2016 The Author(s). All rights reserved. This paper was published by CUER Workshop Proceedings in18th International Workshop on Trust in Agent Societies and is made available with permission of The Author(s). 12 p. application/pdf |
| spellingShingle | Crowdsourcing Gaussian DRNTU::Engineering::Computer science and engineering Zhang, Jie Xiang, Qikun Nevat, Ido Zhang, Pengfei Collusion-resistant spatial phenomena crowdsourcing via mixture of Gaussian Processes regression |
| title | Collusion-resistant spatial phenomena crowdsourcing via mixture of Gaussian Processes regression |
| title_full | Collusion-resistant spatial phenomena crowdsourcing via mixture of Gaussian Processes regression |
| title_fullStr | Collusion-resistant spatial phenomena crowdsourcing via mixture of Gaussian Processes regression |
| title_full_unstemmed | Collusion-resistant spatial phenomena crowdsourcing via mixture of Gaussian Processes regression |
| title_short | Collusion-resistant spatial phenomena crowdsourcing via mixture of Gaussian Processes regression |
| title_sort | collusion resistant spatial phenomena crowdsourcing via mixture of gaussian processes regression |
| topic | Crowdsourcing Gaussian DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/87328 http://hdl.handle.net/10220/49471 http://ceur-ws.org/Vol-1578/ |
| work_keys_str_mv | AT zhangjie collusionresistantspatialphenomenacrowdsourcingviamixtureofgaussianprocessesregression AT xiangqikun collusionresistantspatialphenomenacrowdsourcingviamixtureofgaussianprocessesregression AT nevatido collusionresistantspatialphenomenacrowdsourcingviamixtureofgaussianprocessesregression AT zhangpengfei collusionresistantspatialphenomenacrowdsourcingviamixtureofgaussianprocessesregression |