计算机科学与探索2016，Vol.10Issue(4)：481-494,14.DOI:10.3778/j.issn.1673-9418.1507074
基于矩阵机制的差分隐私连续数据发布方法
Method Based on Matrix Mechanism for Differential Privacy Continual Data Release
摘要
Abstract
The vast majority of the literature on differential privacy algorithms focuses on one time static release of data-sets, while many applications of data analysis involve the continual data release. This paper proposes a method based on matrix mechanism for differential privacy continual data release. The key idea of the proposed method is to firstly construct the strategy matrix of the continual data release problem using the binary indexed tree, and then optimize the strategy matrix to boost the accuracy of the published data. After that, aiming at the high time complexity of existing optimization algorithm based on matrix mechanism, this paper puts forward a fast diagonal matrix optimization algo-rithm (FDA) with O(lg N) time complexity, which can be applied to the situation of large-scale continuous data pub-lishing effectively. This paper compares and analyzes FDA and the traditional algorithms on the accuracy of the released data by experiments. The experimental results show that FDA is effective and feasible.关键词
差分隐私/矩阵机制/树状数组/连续发布Key words
differential privacy/matrix mechanism/binary indexed tree/continual data release分类
计算机与自动化引用本文复制引用
蔡剑平,吴英杰,王晓东..基于矩阵机制的差分隐私连续数据发布方法[J].计算机科学与探索,2016,10(4):481-494,14.基金项目
The National Natural Science Foundation of China under Grant No.61300026(国家自然科学基金) （国家自然科学基金）
the Natural Science Foundation of Fujian Province under Grant No.2014J01230(福建省自然科学基金) （福建省自然科学基金）
