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	<event:Event rdf:about="http://ebiquity.umbc.edu/event/html/id/155/A-Trust-Based-Framework-for-Secure-Data-Aggregation-in-Wireless-Sensor-Networks">
		<rdfs:label><![CDATA[A Trust Based Framework for Secure Data Aggregation in Wireless Sensor Networks]]></rdfs:label>
		<event:title><![CDATA[A Trust Based Framework for Secure Data Aggregation in Wireless Sensor Networks]]></event:title>
		<event:speaker>
<person:Collaborator rdf:about="http://ebiquity.umbc.edu/person/html/Sajal/Dal"><person:name><![CDATA[Sajal Dal]]></person:name><rdfs:label><![CDATA[Sajal Dal]]></rdfs:label></person:Collaborator>
		</event:speaker>
		<event:startDate rdf:datatype="&xsd;dateTime">2006-05-10T12:00:00-05:00</event:startDate>
		<event:location><![CDATA[ITE 325b]]></event:location>
		<event:abstract><![CDATA[In unattended and hostile environments, node compromise can
become a disastrous threat to wireless sensor networks and
introduce uncertainty in the aggregation results. A compromise
node often tends to completely reveal its secrets to an adversary
which in turn renders purely cryptography-based approaches
vulnerable. How to secure the information aggregation process
against node compromise attacks and quantify the uncertainty in
the aggregation results has become an important research
issue. In this talk, we will address this problem by proposing a
trust based framework, rooted in statistics and other distinct
yet closely coupled techniques. The trustworthiness (reputation)
of individual sensor nodes is evaluated with the help of an
information theoretic measure, Kullback-Leibler distance, that
identifies the compromised nodes through an unsupervised learning
algorithm. Upon aggregation, an opinion (metric defining the
degree of belief) is generated to represent the uncertainty in the
aggregation result.
<p>
As the result is disseminated and assembled through the routes to
the sink, the opinion will be propagated and regulated by
Josang's belief model. Our trust based framework effectively
quantifies the uncertainty in the data as well as aggregation
results. Experimental results demonstrate that this framework
provides a powerful mechanism for detecting compromised nodes and
reasoning about uncertainty in the network. It can also purge
false data to accomplish robust aggregation in the presence of
compromised nodes. ]]></event:abstract>
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<person:Collaborator rdf:about="http://ebiquity.umbc.edu/person/html/Dhananjay/Phatak"><person:name><![CDATA[Dhananjay Phatak]]></person:name><rdfs:label><![CDATA[Dhananjay Phatak]]></rdfs:label></person:Collaborator>
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