A&S Graduate Studies
[PAST EVENT] Xin Ruan - Dissertation Defense, Computer Science
Abstract:
With billions of users, Online Social Networks (OSNs) are amongst the largest scale communication applications on the Internet. OSNs enable users to easily access news from local and world-wide, as well as share information publicly and interact with friends. On the negative side, OSNs are also abused by spammers to distribute ads or malicious information, such as scams, fraud, and even manipulate public political opinions. Having achieved significant commercial success with large amount of user information, OSNs do treat the security and privacy of their users seriously and provide several mechanisms to reinforce their account security and information privacy. However, the efficacy, effectiveness and security of these measures are either not thoroughly studies or in need to be improved. In sight of cyber criminals and potential privacy threats on OSNs, we focuses on the evaluations and improvements of OSN user privacy configurations, account security protection mechanisms and trending topic security in this dissertation.
In the first project, we examine the effectiveness of OSN privacy settings on protecting user privacy. Given each privacy configuration, we propose a corresponding scheme to reveal the target user's basic profile and connection information starting from some leaked connections on its homepage. Based on the dataset we collect on Facebook, we calculate the privacy exposure in each privacy setting type and measure the accuracy of our privacy inference schemes given different amount of public information. The evaluation results show that users' private basic profile can be inferred with high accuracy and connections can be revealed in a significant portion based on even a small number of directly leaked connections.
In the second project, we propose a behavioral-profile-based method to detect OSN user account compromisation in a timely manner. Specifically, we propose eight behavioural features to portray a user's social behaviour. A user's statistical distributions of those feature values comprise its behavioral profile. Based on the sample data we collect from Facebook, we find that each user's activities are highly likely to conform to its behavioural profile while different user's profile tend to diverge from each other, which can be employed for compromisation detection. The evaluation result shows that the more complete and accurate a user's behavioral profile can be built the more accurately compromisation can be detected.
Thirdly, we investigate the manipulation of OSN trending topics. Based on the dataset we collect from Twitter, we manifest the manipulation of trending and a suspect spamming infrastructure. We then measure how accurately the five factors (popularity, coverage, transmission, potential coverage, and reputation) can predict trending using an SVM classifier. We further study the interaction patterns between authenticated accounts and malicious accounts in trending. At last we show the threat of compromised accounts and sybil accounts to trending using simulation and discuss countermeasures against trending manipulation.
Bio:
Xin Ruan is a Ph.D. candidate in the Department of Computer Science at William & Mary. She is working with Dr. Haining Wang. Her research interests include security and privacy issues in online social networks, data analysis. Before she came to William and Mary she received her M.S. and B.S. in Computer Science from Xidian University in 2009 and 2007 respectively.