计算机科学 ›› 2019, Vol. 46 ›› Issue (9): 99-105.doi: 10.11896/j.issn.1002-137X.2019.09.013
吴英杰, 黄鑫, 葛晨, 孙岚
WU Ying-jie, HUANG Xin, GE Chen, SUN Lan
摘要: 当前许多实际应用需要持续地对流数据的区间统计查询做出实时响应,并使用差分隐私保护模型来应对信息发布过程中的敏感数据泄露问题。现有研究采用树状数组作为组织和存储流数据的数据结构,以满足信息发布的实时性要求。然而,现有方法中的相关参数为预先确定的,并不能很好地适应查询的动态变化。为此,文中提出在流数据实时发布的框架上,引入历史查询信息,以实现发布过程中树高参数的动态调整。首先,使用移动平均法分析历史查询记录,并预测后续的查询范围分布;继而针对预测结果,通过理论推导,得出使得期望误差最小的树高;最终实现差分隐私流数据实时发布中树高参数的自适应优化。实验结果表明,该方法在保证了时间效率的同时,有效地提高了发布结果的精度。
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