People store and share ever-increasing numbers of digital documents, photos, and other files, both on personal devices and within online services. In this environment, proper access control is critical to help users obtain the benefits of sharing varied content with different groups of people while avoiding trouble at work, embarrassment, identity theft, and other problems related to unintended disclosure. Current approaches often fail, either because they insufficiently protect data or because they confuse users about policy specification. Historically, correctly managing access control has proven difficult, timeconsuming, and error-prone, even for experts; to make matters worse, access control remains a secondary task most non-experts are unwilling to spend significant time on.
To solve this problem, access control for file-sharing tools and services should provide verifiable security, make policy configuration and management simple and understandable for users, reduce the risk of user error, and minimize the required user effort. This thesis presents three user studies that provide insight into people’s access-control needs and preferences. Drawing on the results of these studies, I present Penumbra, a prototype distributed file system that combines semantic, tag-based policy specification with logicbased access control, flexibly supporting intuitive policies while providing high assurance of correctness. Penumbra is evaluated using a set of detailed, realistic case studies drawn from the presented user studies. Using microbenchmarks and traces generated from the case studies, Penumbra can enforce users’ policies with overhead less than 5% for most system calls. Finally, I present lessons learned, which can inform the further development of usable access-control mechanisms both for sharing files and in the broader context of personal data.
| Identifer | oai:union.ndltd.org:cmu.edu/oai:repository.cmu.edu:dissertations-1325 |
| Date | 01 May 2014 |
| Creators | Mazurek, Michelle L. |
| Publisher | Research Showcase @ CMU |
| Source Sets | Carnegie Mellon University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations |
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