Privacy-Preserving Community-Aware Trending Topic Detection in Online Social Media

被引：5

作者：

Georgiou, Theodore ^{[1
]}

El Abbadi, Amr ^{[1
]}

Yan, Xifeng ^{[1
]}

机构：

[1] Univ Calif Santa Barbara, Dept Comp Sci, Santa Barbara, CA 93106 USA

来源：

DATA AND APPLICATIONS SECURITY AND PRIVACY XXXI, DBSEC 2017 | 2017年 / 10359卷

关键词：

K-ANONYMITY; GRAPHS;

D O I：

10.1007/978-3-319-61176-1_11

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Trending Topic Detection has been one of the most popular methods to summarize what happens in the real world through the analysis and summarization of social media content. However, as trending topic extraction algorithms become more sophisticated and report additional information like the characteristics of users that participate in a trend, significant and novel privacy issues arise. We introduce a statistical attack to infer sensitive attributes of Online Social Networks users that utilizes such reported community-aware trending topics. Additionally, we provide an algorithmic methodology that alters an existing community-aware trending topic algorithm so that it can preserve the privacy of the involved users while still reporting topics with a satisfactory level of utility.

引用

页码：205 / 224

页数：20