Abstract
Spatial Crowdsourcing (SC) is a transformative platform that engages a crowd of mobile users (i.e., workers) in collecting and analyzing environmental, social and other spatio-temporal information. However, current solutions ignore the preference of each worker’s remuneration and acceptable distance, and the lack of error analysis after privacy control lead to undesirable task recommendation. In this paper, we introduce an optimization framework for task recommendation while protecting participant privacy. We propose a Generalization mechanism based on Bisecting k-means and an efficient algorithm considering the generalization error to maximization the reward of SC server. Both numerical evaluations and performance analysis are conducted to show the effectiveness and efficiency of the propose framework.
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This article is partly supported by the National Natural Science Foundation of China under Grant No. 61370084, and the China Numerical Tank Project.
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Lu, D., Han, Q., Zhao, H., Zhang, K. (2017). Optimal Task Recommendation for Spatial Crowdsourcing with Privacy Control. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-6385-5_35
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DOI: https://doi.org/10.1007/978-981-10-6385-5_35
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