Sensitivity Reduction Of Degree Histogram Publication Under Node Differential Privacy Via Mean Filtering
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE(2021)
摘要
Publication of nodes' degree information in the form of histogram provides useful information about the graph as well as the risk of privacy disclosure. Under the robust protection of node-differential privacy (node-DP), publishing result's accuracy mainly depends on the global sensitivity of this publishing task. Thus, the reduction of sensitivity is of great importance. Existing methods for degree histogram publication under node-DP are mostly based on limitation of maximum degree, whose sensitivity is still high, leading an unbearable noise scale. In this paper, we innovatively propose a method to tackle this issue. Firstly, we introduce mean filtering to process the histogram, almost halve the original sensitivity. Then, we use a series of techniques to further improve publishing accuracy, instituting a complete workflow for degree histogram publication under node-DP. Experimental results show that our method effectively improves the accuracy.
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关键词
degree histogram publication, differential privacy, global sensitivity, mean filtering
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