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Companion of the 30th international conference on Software engineering
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EVT'08 Proceedings of the conference on Electronic voting technology
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IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
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EVT/WOTE'09 Proceedings of the 2009 conference on Electronic voting technology/workshop on trustworthy elections
Computer vulnerability evaluation using fault tree analysis
ISPEC'05 Proceedings of the First international conference on Information Security Practice and Experience
Using attack trees to identify malicious attacks from authorized insiders
ESORICS'05 Proceedings of the 10th European conference on Research in Computer Security
Definition and analysis of election processes
SPW/ProSim'06 Proceedings of the 2006 international conference on Software Process Simulation and Modeling
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EVT/WOTE'11 Proceedings of the 2011 conference on Electronic voting technology/workshop on trustworthy elections
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EVT/WOTE'12 Proceedings of the 2012 international conference on Electronic Voting Technology/Workshop on Trustworthy Elections
Turtles all the way down: a clean-slate, ground-up, first-principles approach to secure systems
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This paper presents an approach for continuous process improvement and illustrates its application to improving the robustness of election processes. In this approach, the Little-JIL process definition language is used to create a precise and detailed model of an election process. Given this process model and a potential undesirable event, or hazard, a fault tree is automatically derived. Fault tree analysis is then used to automatically identify combinations of failures that might allow the selected potential hazard to occur. Once these combinations have been identified, we iteratively improve the process model to increase the robustness of the election process against those combinations that seem the most likely to occur. We demonstrate this approach for the Yolo County election process. We focus our analysis on the ballot counting process and what happens when a discrepancy is found during the count. We identify two single points of failure (SPFs) in this process and propose process modifications that we then show remove these SPFs.