@conference {74,
	title = {Context-aware Multimedia Encryption in Mobile Platforms},
	booktitle = {Proceedings of the 9th Annual Cyber and Information Security Research Conference},
	year = {2014},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {<p>Voice over IP (VoIP) is a part of multimedia content and this communication is growing rapidly in portable devices due to its attractive features. Secure communication is critical as most of such communications are routed through public channels. Establishing secure VoIP communication is computationally expensive. Modern high strength encryption algorithms such as AES, RSA, Serpent, etc. need high computational power for encryption. On the mobile platform this is a significant factor due to its constrained power resources. In this work we are presenting a novel idea of encrypting VoIP speech data while conserving resources without compromising the security of the VoIP communication. First, the algorithm detects the content of the speech data and identifies its context. Then sensitive sections of the conversation are separated. In the last step, these sections are encrypted with a high strength cryptographic algorithm while other parts are encrypted with a less strength algorithm for better performance. In this way unnecessary encryption power is spared. This will conserve computational power while ensuring the security of sensitive information. Also, we discuss some of the crucial potential attack vectors and their defense mechanisms. Finally, we implemented a prototype to test our concept and we observed an average of 39\% reduction in computational time.</p>
},
	keywords = {Android, context-aware, encryption, speech recognition, VoIP},
	isbn = {978-1-4503-2812-8},
	doi = {10.1145/2602087.2602115},
	url = {http://doi.acm.org/10.1145/2602087.2602115},
	author = {Fazeen, Mohamed and Bajwa, Garima and Ram Dantu}
}
@conference {87,
	title = {Cerebral Autoregulation Assessment Using Electroencephalograms},
	booktitle = {Proceedings of the 8th International Conference on Body Area Networks},
	year = {2013},
	publisher = {ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)},
	organization = {ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)},
	address = {ICST, Brussels, Belgium, Belgium},
	abstract = {<p>This paper presents the possibility of using Electroencephalograms (EEG) signals of an individual for quantitative interpretation of Cerebral Autoregulation (CA). EEG data was recorded during arm cuff inflation to induce dynamic changes in arterial blood pressure and then, Cerebral Blood Flow (CBF) was estimated from EEG using canonical hemodynamic response function (HRF). The assessment of CA was carried out by using power of the various frequency bands of EEG signal.</p>
},
	keywords = {Blood pressure, cerebral autoregulation, cerebral blood flow, electroencephalograms},
	isbn = {978-1-936968-89-3},
	doi = {10.4108/icst.bodynets.2013.253703},
	url = {http://dx.doi.org/10.4108/icst.bodynets.2013.253703},
	author = {Bajwa, Garima and Ram Dantu}
}
