Type
Text
Type
Dissertation
Advisor
Scott Stoller | Rob Johnson. Yejin Choi. | MÇünica F. Fernandez-Bugallo.
Date
2011-05-01
Keywords
Computer Security, Human-Computer Interaction, Machine Learning, Natural Language Processing, Psychology | Computer Science
Department
Department of Computer Science
Language
en_US
Source
This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.
Identifier
http://hdl.handle.net/11401/71616
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
Abstract
In many contexts, users are either unable or unwilling to specify their access control policies. In Data Loss Prevention, for example, users cannot fully express what is secret in rule-based formats. Many users are unwilling to use access controls, particularly in the Web 2.0, because they are too draconian, leading to disastrous consequences in terms of privacy. To address both of these issues, we have introduced the concept of Content-Based Access Control (CBAC). CBAC combines content recognition with policy acquisition and enforcement. A CBAC-enabled system can be trained to recognize policy violations by learning what is secret from examples. This defense will discuss how CBAC can be successfully applied to Data Loss Prevention, Wikipedia Vandalism and the Web 2.0. Usability is integral to providing better CBAC systems and privacy interfaces, and this dissertation demonstrates improvements in the usability of these systems.
Recommended Citation
Hart, Michael Andrew, "Content-based Access Control" (2011). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 821.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/821