"Framework for Security and Privacy in Automotive Telematics"

	Automotive telematics are information intensive applications that are enabled for vehicles by
combination of telecommunications and computing technology. These applications may report to the driver
traffic information, GPS location, etc. In order for automotive telematics to grow, telematics data 
must be protected. Providers must know that the data received is accurate and that end users know that 
their privacy is assured. 

	There are security and privacy issues which are unique to automotive telematics. Automobiles 
are sensor-rich environments, thus in addition to static data, such as vehicle identification information,
a significant amount of data generated in the vehicle is dynamic. The sheer volume of the dynamic data 
generated makes it difficult, if not impossible, to store it within the automobile itself. As a result,
decisions about what to store, and where, become very important. This issue is amplified by the privacy 
concern of data storage.

	This paper proposes a framework, Data Protection Framework, to enable building telematics 
computing platforms that can be trusted by both users and service providers. This framework uses 
defense-in-depth approach to build secure platform from the ground up, enables data aggregation close 
to source on the computing system trusted by the user, and uses user defined privacy policies for obtaining 
user consent before data collection and usage. In defense-in-depth approach, each layer of hardware and 
software provides its own security functions. This scheme assures end-to-end and life cycle protection of 
relevant data only when the same level of security is employed across the entire system. User trust can be 
further enhanced by minimizing the amount of private data that leaves the computing system trusted by the 
user. Service providers deploy data aggregation applications inside the computing system. Only aggregated 
results are sent back to the service provider. Finally, the framework enforces privacy policies defined by user
by classifying data and defining data handling rules.

	Given a well designed interface, this framework may inspire confidence in end users. However, ultimately,
the information requested by service providers is sent over the radio waves and is susceptible to interception.
Furthermore, intercepting automotive telematics data is made easier with data aggregation. Little cleanup or parsing
is required since all relevant data is aggregated and sent in one chunk.