面向数据流的差分隐私直方图发布

摘要

针对现有数据流相关的差分隐私发布技术主要应用于二进制数据流,不能高效地处理一般性数据流发布中隐私的问题,提出一种高效、面向分布不均匀的数值型数据流的差分隐私直方图发布算法——DDPA. 该算法基于滑动窗口模型,利用相邻2个时间戳的数据集分布的相似性,动态合理分配隐私预算,使得每一个窗口的总预算不超过隐私预算ε,并利用分组与合并策略,快速计算出局部最优直方图. 通过对该算法发布数据的可用性与同类算法进行比较分析,实验结果表明,该算法是有效可行的.%Current research on differential private publication associcted with data stream mainly considers a binary data stream , which cannot efficiently deal with the general data stream '' s private publication .An efficient differential private histogram publica-tion algorithm called DDPA was proposed , which is oriented toward non-uniform distributed numerical stream .Basing on the slid-ing window model , the similarity on two adjacent timestamps of data distribution is applied to allocate the budget privacy dynami -cally, which makes each window ''s total budge not exceed the privacy budget ε, and after that, the grouping and merging strate-gies are used to calculate the local optimal histogram quickly .According to comparing and analyzing the proposed algorithm with the other similar algorithms on the published data '' s availability , the experimental results show that the proposed algorithm is effective and feasible .