Differentially private high-dimensional data publication via grouping and truncating techniques  

作　　者：Ning WANG Yu GU Jia XU Fangfang LI Ge YU 

机构地区：[1]School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China [2]School of Computer, Electronics and Information, Guangxi University, Guangxi 530004, China

出　　处：《Frontiers of Computer Science》2019年第2期382-395,共14页中国计算机科学前沿（英文版）

基　　金：the National Natural Science Foundation of China (Grant Nos. 61433008, 61472071 and U143520006);the Fundamental Research Funds for the Central Universities of China (161604005 and 171605001);the Natural Science Foundation of Liaoning Province (2015020018).

摘　　要：The count of one column for high-dimensional datasets, i.e., the number of records containing this column, has been widely used in nuinerous applications such as analyzing popular spots based on check-in location information and mining valuable items from shopping records. However, this poses a privacy threat when directly publishing this information. Differential privacy (DP), as a notable paradigm for strong privacy guarantees, is thereby adopted to publish all column counts. Prior studies have verified that truncating records or grouping columns can effectively improve the accuracy of published results. To leverage the advantages of the two techniques, we combine these studies to further boost the accuracy of published results. However, the traditional penalty function, which measures the error imported by a given pair of parameters including truncating length and group size, is so sensitive that the derived parameters deviate from the optimal parameters significantly. To output preferable parameters, we first design a smart penalty function that is less sensitive than the traditional function. Moreover, a two-phase selection method is proposed to compute these parameters efficiently, together with the improvement in accuracy. Extensive experiments on a broad spectrum of real-world datasets validate the effectiveness of our proposals.

关 键 词：differential privacy HIGH-DIMENSIONAL data TRUNCATION optimization GROUPING PENALTY function 

分 类 号：TP[自动化与计算机技术]