Title :
Content knowledge based privacy estimation model for anonymous OSN data publishing
Author :
Cheng Cheng ; Chunhong Zhang ; Qingyuan Hu
Author_Institution :
Beijing Univ. of Posts & Telecommun., Beijing, China
Abstract :
Online Social Network (OSN) data is often collected by the third parties for various purposes. One of the problems in such practices is how to measure the privacy breach to assure secure users. However, the recent works on privacy estimation are not systematic enough and are mainly focus on the traditional datasets, such as bank data and hospital data. Compared with these closed environments, the open APIs and lower register barriers make OSNs an open environment. Thus the openness of OSN makes more User Generated Content (UGC) like blogs and remarks be achieved easily by adversaries. In this paper, we analyzed the background knowledge in OSNs and proposed a general privacy estimation model facing OSNs data based on linear regression. In particular, our model takes the content knowledge of adversary into consideration. Considered the high dimension of content knowledge, which could cause high computational overhead, we optimized our model by Principal Component Analysis (PCA).
Keywords :
application program interfaces; content management; data privacy; principal component analysis; regression analysis; social networking (online); PCA; UGC; anonymous OSN data publishing; background knowledge analysis; bank data; blogs; content knowledge based privacy estimation model; hospital data; linear regression; online social network data; open APIs; principal component analysis; register barriers; user generated content; Data models; Data privacy; Estimation; Mathematical model; Principal component analysis; Privacy; Publishing; background knowledge; principal component analysis; privacy estimation; user generated content;
Conference_Titel :
Communications and Networking in China (CHINACOM), 2013 8th International ICST Conference on
Conference_Location :
Guilin
DOI :
10.1109/ChinaCom.2013.6694636