@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 = {<p>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\%.</p>
},
	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}
}
@conference {45,
	title = {Predicting Calls: New Service for an Intelligent Phone},
	booktitle = {10th IFIP/IEEE International Conference on Management of Multimedia and Mobile Networks and Services, MMNS 2007},
	year = {2007},
	month = {11/2007},
	address = { San Jos{\'e}, USA},
	abstract = {<p>Predicting future calls can be the next advanced feature of the intelligent phone as the phone service providers are looking to offer new services to their customers. Call prediction can be useful to many applications such as planning daily schedule and attending unwanted communications (e.g. voice spam). Predicting calls is a very challenging task. We believe that this is a new area of research. In this paper, we propose a Call Predictor (CP) that computes the probability of receiving calls and makes call prediction based on caller{\textquoteright}s behavior and reciprocity. The proposed call predictor is tested with the actual call logs. The experimental results show that the call predictor performs reasonably well with false positive rate of 2.4416\%, false negative rate of 2.9191\%, and error rate of 5.3606\%.</p>
},
	keywords = {Arrival time, behavior, call matrix, Callee, Caller, Communications, Incoming calls, Inter-arrival time, Inter-arrival/departure time, Kernel density estimation, Outgoing calls, Probability density function (pdf), Receiving call probability, Reciprocity},
	doi = {10.1007/978-3-540-75869-3\_3},
	author = {Phithakkitnukoon, Santi and Ram Dantu}
}
