@conference {101, title = {Current and Future Trends in Social Media}, booktitle = {2011 IEEE Third Int{\textquoteright}l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int{\textquoteright}l Conference on Social Computing}, year = {2011}, abstract = {

Social networking has been the biggest cultural shift since the industrial revolution, attracting millions of people, creating an ever-expanding social web, and revolutionizing the way we communicate. Although many changes have occurred since the fledgling steps of social networking and what it has evolved into today, improvement is a never-ending cycle. Thus, in this paper, additional methods will be explored to advance social networking overall.

}, keywords = {Buildings, Electronic mail, Facebook, Media, Online Social Networks, Privacy, Social Media, Social Network Trends, Twitter}, doi = {10.1109/PASSAT/SocialCom.2011.125}, author = {Baatarjav, Enkh-Amgalan and Ram Dantu} } @conference {75, title = {Unveiling Hidden Patterns to Find Social Relevance}, booktitle = {Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on}, year = {2011}, month = {Oct}, abstract = {

Twitter is both a useful social networking device and an incredible marketing tool. However, it is also a venue for dangerous stalkers and a sub-world of internet users that most people would intuitively avoid if seeing them in real life. It would improve Twitter{\textquoteright}s safety to have filters available which would allow users to select an audience for their status updates without being forced into changing their profiles to a private setting. The hypothetical filter, or model, studied in this paper was based on two particular attributes: activity correlations and vocabulary similarities between users and followers. If implemented, this model would restrict the availability of status updates to an automatically generated group of socially relevant followers. The result of this study shows that both of the attributes can be used to define social relevance, however, it was found that activity patterns have better predictive capabilities than correlating vocabulary usage between users and followers.

}, keywords = {activity correlations, Correlation, dangerous stalkers, data privacy, Facebook, hidden patterns, Internet, Internet users, marketing tool, Online Social Networks, Polynomials, Privacy, social networking (online), social networking device, social relevance, Twitter, Twitter safety, Vocabulary, vocabulary similarities}, doi = {10.1109/PASSAT/SocialCom.2011.103}, author = {Baatarjav, Enkh-Amgalan and Ram Dantu} } @conference {57, title = {Are You My Friend?}, booktitle = {Proceedings of the 7th IEEE Conference on Consumer Communications and Networking Conference}, year = {2010}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {Piscataway, NJ, USA}, abstract = {

With Twitters growing popularity, privacy has become a major concern for users wary of sensitive information falling in the wrong hands. A typical Twitter user carries hundreds of followers - people who have subscribed to users twitter feeds. Our goal is to target followers that a Twitter user deems safe. Therefore, selecting followers with a closer relationship could help decrease the risk of sensitive information being sent to unknown people. We propose a privacy management system that helps a Twitter user restrict information to only certain followers based on the strength of their social tie. This system would incorporate two tools: the Exclusivity meter and the Twitter Response Estimator. The Exclusivity Meter employs user{\textquoteright}s activity profile to guess similarities between users. Preliminary results have indicated that similarity in time and level of activity between user and follower does suggest a stronger social tie. The Twitter Response Estimator uses a measure of prestige to gauge the probability of response. When applied to a set of followers, the estimator separates real friends from recreational followers

}, keywords = {online social network, Privacy, Twitter}, isbn = {978-1-4244-5175-3}, url = {http://dl.acm.org/citation.cfm?id=1834217.1834342}, author = {Baatarjav, Enkh-Amgalan and Amin, Aliasgar and Ram Dantu and Gupta, Nikhil Kant} } @conference {30, title = {Privacy Management for Facebook}, booktitle = {4th International Conference on Information Systems Security (ICISS 2008)}, year = {2008}, month = {12/2008}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, address = {Hyderabad, India}, abstract = {

As more people adopt the Internet as a medium of communication, the Internet has developed into a virtual world and this has resulted in many online social networks (SN). MySpace and Facebook, two leading online SN sites, have a combined user base of 170 million as of 2008. SN sites started to offer developers open platforms that provide users{\textquoteright} profile information to the developers. Unfortunately, the applications can also be used to invade privacy and to harvest the users{\textquoteright} profile information without their acknowledgement. To address this vulnerability, we propose a privacy-management system that protects the accessibility of users{\textquoteright} profile. The system uses probabilistic approach based on information revelation of users. Our experimental result shows that the system can achieve high accuracy rate of 75\%.

}, keywords = {Facebook, Privacy, Privacy management, Social network}, doi = {10.1007/978-3-540-89862-7\_23}, author = {Baatarjav, Enkh-Amgalan and Ram Dantu and Phithakkitnukoon, Santi} }