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dc.contributor.advisorLalana Kagal.en_US
dc.contributor.authorMiao, Daniela Yidanen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2015-02-25T16:43:31Z
dc.date.available2015-02-25T16:43:31Z
dc.date.copyright2014en_US
dc.date.issued2014en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/95523
dc.descriptionThesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, Engineering Systems Division, 2014.en_US
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 71-73).en_US
dc.description.abstractWith the advent of "smart" mobile phones and ubiquitous mobile applications, the pace at which people generate, access, and acquire data has accelerated significantly. In this thesis, we first examine how privacy issues in the mobile apps market compromise the well-being of both app consumers and developers, noting that one important problem is the lack of usable privacy policies. Subsequently, we propose a technical solution named PrivacyInformer that automatically generates mobile app privacy descriptions, thereby relieving developers the burden of manually creating them. This tool is implemented as an extension to the MIT App Inventor, a do-it-yourself mobile app building platform that has a vast international user base, as well as a growing impact on the democratizing of mobile app building. We show that by analyzing source code of mobile apps directly in App Inventor, PrivacyInformer can produce simple and useful privacy descriptions in both human-readable and machine-readable format. Specifically, these generated documents describe how mobile apps use private information, rather than simply enumerating a list of data access as done in the permissions system. Finally, we conduct an exploratory user study to evaluate the effectiveness of PrivacyInformer from the app developer's perspective, as well as discuss the policy impact of such a tool in the mobile app development community.en_US
dc.description.statementofresponsibilityby Daniela Yidan Miao.en_US
dc.format.extent73 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectEngineering Systems Division.en_US
dc.subjectTechnology and Policy Program.en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titlePrivacyInformer : an automated privacy description generator for the MIT App Inventoren_US
dc.title.alternativeAutomated privacy description generator for the MIT App Inventoren_US
dc.typeThesisen_US
dc.description.degreeS.M. in Technology and Policyen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.contributor.departmentMassachusetts Institute of Technology. Engineering Systems Division
dc.identifier.oclc903649516en_US


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