Thesis
Gridaba-dynamic attribute based approach for fine grained access control of powergrid data
Washington State University
Master of Science (MS), Washington State University
2015
Handle:
https://hdl.handle.net/2376/103460
Abstract
Trusted, reliable communication is vital to operating a modern electric grid. There are many different data sources required by power grid applications with widely varying access restrictions. For example, a Phasor Measurement Unit (PMU) data source may have very different restrictions from stored topologies and models. Another major challenge with existing power grid data management platforms is the tight coupling of data source and the application (e.g., a data analysis algorithm). GridOPTICSTM Software System (GOSS) middleware is designed as a research prototype platform for flexible and adaptable power grid data management. GOSS de-couples the application from the data source allowing them to evolve independently. To meet the fine-grained access control needs identified within the power grid, I have developed the Grid Attribute Based Access (GridABA) architecture within GOSS based on attribute based access control (ABAC). This provides an extensible framework for each data source and application to have differing security restrictions. It utilizes permissions based on attributes contained within the data (originating sensor, age) as well as request level attributes (level of access, operation), and other environment attributes to provide secure data access for a wide range of powergrid data sources. This allows for the enforcement of complex authorization policies and permissions in time series data defined by regulations or data owners.
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Details
- Title
- Gridaba-dynamic attribute based approach for fine grained access control of powergrid data
- Creators
- Tara Danielle Gibson
- Contributors
- David Bakken (Degree Supervisor)
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University; [Pullman, Washington] :
- Identifiers
- 99900525156901842
- Language
- English
- Resource Type
- Thesis