关键词: 差分隐私, 流数据发布, 查询效率, 矩阵机制

Abstract:

At present, many of the practical applications associated with streaming data require a large number of real-time queries, and existing solutions cannot meet the efficiency requirements of a large number of real-time queries. To this end, this paper presents a real-time release of differential privacy streaming data. The basic idea is to use the tree array to construct the statistical release model corresponding to the streaming data in the sliding window. The model can realize the continuous statistical release under the sliding window in the linear time, and then the linear combination of the results can be published by the continuous statistics. In the O(1) time it can obtain any interval query results which user needs; then, using the advantage of matrix in dealing with relevance query, the query accuracy is further improved by using diagonal matrix optimization under the condition that the efficiency of the query is the same order of magnitude. The query efficiency and query accuracy of the proposed algorithm are compared with the other arbitrary interval query release algorithms in the sliding window. The experimental results show that the method can improve the query efficiency and has better query accuracy.

Key words: differential privacy, streaming data publication, query efficiency, matrix mechanism