不规则线段树的差分隐私位置隐私保护方法

Differential Privacy of Location Privacy Protection Method for Irregular Segment Tree

差分隐私因具有严格推理和证明的隐私保证,常被应用于位置隐私保护场景中.用户进行位置连续查询时,会引起噪声叠加导致查询精度下降,目前基于规则树结构的差分隐私虽然能降低查询误差,但会产生大量无效零节点,数据结构过大,在查询精度上还有进一步提高的空间.本文提出了不规则线段树的差分隐私位置隐私保护方法,将不规则线段树引入差分隐私方法中,根据节点覆盖率和Laplace机制的敏感度推导出不规则线段树的估值函数,从而筛选出较优的不规则线段树结构.该方法能有效减小连续查询时噪声叠加带来的查询精度下降的问题,相对于其他提高差分隐私查询精度的方法有更小的查询误差,并能适应不同密度环境的LBS位置查询服务.

Differential privacy is often used in location privacy protection scenarios because of its strict reasoning and proof privacy guarantee.When users make continuous query in mobile environment,it will cause noise superposition and decrease query accuracy.At present,although the differential privacy method based on rule tree structure can reduce the query error,it will cause a large number of invalid zero nodes and the data structure is too large,so there is room for further improvement in improving the query accuracy.This paper proposes a differential privacy of location privacy protection method for irregular segment tree,introduces the irregular segment tree into the differential privacy method,and deduces the evaluation function of the irregular line segment trees according to the node coverage rate and the sensitivity of Laplace mechanism,so as to screen out the optimal structure of the irregular segment trees.Compared with other methods to improve the accuracy of differential privacy query,this method has smaller query errors and can adapt to LBS location query services in different density environments.