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Data collaborations allow users to draw upon diverse resources to solve complex problems. While collaborations enable a greater ability to manipulate data and services, they also create new security vulnerabilities. Collaboration participants need methods to detect suspicious behaviors (potentially caused by malicious insiders) and assess trust in information when it passes through many hands. In this paper, the authors describe these challenges and introduce provenance as a way to solve them. They describe a provenance system, PLUS, and show how it can be used to assist in assessing trust and detecting suspicious behaviors. A preliminary study shows this to be a promising direction for future research.
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