Optimal Task Recommendation for Spatial Crowdsourcing with Privacy Control

• 作者: Dan Lu Hongbin Zhao Kejia Zhang Qilong Han
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: SPATIAL crowdsourcing TASK RECOMMENDATION PRIVACY CONTROL REWARD
• 年，卷（期）: 2017,（1）
• 页码范围: 104-106
• 页数: 3页
• 分类号: C5

摘要

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.