Differentially Private Data Publishing and Analysis: A Survey

被引：226

作者：

Zhu, Tianqing ^{[1
]}

Li, Gang ^{[1
]}

Zhou, Wanlei ^{[1
]}

Yu, Philip S. ^{[2
,3
]}

机构：

[1] Deakin Univ, Sch Informat Technol, Burwood 3125, Australia

[2] Univ Illinois, Dept Comp Sci, Chicago, IL 60607 USA

[3] Tsinghua Univ, Inst Data Sci, Beijing 100084, Peoples R China

来源：

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | 2017年 / 29卷 / 08期

基金：

美国国家科学基金会;

关键词：

Differential privacy; privacy preserving data publishing; privacy preserving data analysis; ALGORITHMS; COMPLEXITY; FRAMEWORK; QUERIES;

D O I：

10.1109/TKDE.2017.2697856

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Differential privacy is an essential and prevalent privacy model that has been widely explored in recent decades. This survey provides a comprehensive and structured overview of two research directions: differentially private data publishing and differentially private data analysis. We compare the diverse release mechanisms of differentially private data publishing given a variety of input data in terms of query type, the maximum number of queries, efficiency, and accuracy. We identify two basic frameworks for differentially private data analysis and list the typical algorithms used within each framework. The results are compared and discussed based on output accuracy and efficiency. Further, we propose several possible directions for future research and possible applications.

引用

页码：1619 / 1638

页数：20

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