Privacy-Preserving Assessment of Social Network Data Trustworthiness

被引：3

作者：

Dai, Chenyun ^{[1
]}

Rao, Fang-Yu ^{[1
]}

Truta, Traian Marius ^{[2
]}

Bertino, Elisa ^{[1
]}

机构：

[1] Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA

[2] Northern Kentucky Univ, Dept Comp Sci, Highland Hts, KY USA

来源：

PROCEEDINGS OF THE 2012 8TH INTERNATIONAL CONFERENCE ON COLLABORATIVE COMPUTING: NETWORKING, APPLICATIONS AND WORKSHARING (COLLABORATECOM 2012) | 2012年

关键词：

social network; privacy; trustworthiness; ALGORITHM;

D O I：

10.4108/icst.collaboratecom.2012.250417

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Extracting useful knowledge from social network datasets is a challenging problem. To add to the difficulty of this problem, privacy concerns that exist for many social network datasets have restricted the ability to analyze these networks and consequently to maximize the knowledge that can be extracted from them. This paper addresses this issue by introducing the problem of data trustworthiness in social networks when repositories of anonymized social networks exist that can be used to assess such trustworthiness. Three trust score computation models (absolute, relative, and weighted) that can be instantiated for specific anonymization models are defined and algorithms to calculate these trust scores are developed. Using both real and synthetic social networks, the usefulness of the trust score computation is validated through a series of experiments.

引用

页码：97 / 106

页数：10