计算机科学 ›› 2020, Vol. 47 ›› Issue (10): 41-47.doi: 10.11896/jsjkx.200700070
所属专题： 群智感知计算
蔡威, 白光伟, 沈航, 成昭炜, 张慧丽
CAI Wei, BAI Guang-wei, SHEN Hang, CHENG Zhao-wei, ZHANG Hui-li
摘要： 移动群智感知系统需要为用户提供个性化隐私保护,以吸引更多用户参与任务。然而,由于恶意攻击者的存在,用户提升隐私保护力度会导致位置可用性变差,降低任务分配效率。针对该问题,提出了一种基于强化学习的用户与平台共赢的博弈机制。该机制首先通过可信第三方的两个虚拟实体分别模拟用户并与平台进行交互,一个模拟用户选择隐私预算为位置数据添加噪声,另一个模拟平台根据用户的扰动位置分配任务;然后,将交互过程构建为博弈,并推导出均衡点,其中交互的两个虚拟实体就是博弈双方;最后,使用强化学习方法不断尝试不同的位置扰动策略,输出一个最优的位置扰动方案。实验结果表明,该机制能在优化任务分配效用的同时,尽可能地提高用户的整体效用,使用户与平台达成双赢。
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