Modern distributed applications are long-lived, are expected to
provide flexible and adaptive data services, and must meet the
functionality and scalability challenges posed by dynamically changing
user communities in heterogeneous execution environments. The
practical implications of these requirements are that reconfiguration
and upgrades are increasingly necessary, but opportunities to perform
such tasks offline are greatly reduced. Developers are responding to
this situation by dynamically extending or adjusting application
functionality and by tuning application performance, a typical method
being the incorporation of client- or context-specific code into
applications' execution loops.
Our work addresses a basic roadblock in deploying such solutions: the protection of key
application components and sensitive data in distributed applications.
Our approach, termed Dynamic Differential Data Protection (D3P),
provides fine-grain methods for providing component-based protection
in distributed applications. Context-sensitive, application-specific
security methods are deployed at runtime to enforce restrictions in
data access and manipulation. D3P is suitable for low- or
zero-downtime environments, since deployments are performed while
applications run. D3P is appropriate for high performance environments
and for highly scalable applications like publish/subscribe, because
it creates native codes via dynamic binary code generation. Finally,
due to its integration into middleware, D3P can run across a wide
variety of operating system and machine platforms.
This dissertation introduces D3P, using sample
applications from the high performance and pervasive computing domains
to illustrate the problems addressed by our D3P solution. It also
describes how D3P can be integrated into modern middleware. We
present experimental evaluations which demonstrate the fine-grain
nature of D3P, that is, its ability to capture individual end users'
or components' needs for data protection, and also describe the
performance implications of using D3P in data-intensive applications.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/7239 |
| Date | 20 July 2005 |
| Creators | Widener, Patrick M. (Patrick McCall) |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 1102088 bytes, application/pdf |
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