2026-09-19T14:42:40Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/955232022-01-13T07:55:14Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Lalana Kagal.
Miao, Daniela Yidan
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Engineering Systems Division
2015-02-25T16:43:31Z
2015-02-25T16:43:31Z
2014
2014
http://hdl.handle.net/1721.1/95523
903649516
Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, Engineering Systems Division, 2014.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 71-73).
With 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.
by Daniela Yidan Miao.
S.M. in Technology and Policy
S.M.
73 pages
eng
Massachusetts Institute of Technology
M.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.
http://dspace.mit.edu/handle/1721.1/7582
Engineering Systems Division.
Technology and Policy Program.
Electrical Engineering and Computer Science.
PrivacyInformer : an automated privacy description generator for the MIT App Inventor
Automated privacy description generator for the MIT App Inventor
Thesis
application/pdf
Publication
unknown
unknown
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<Abstract>With 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.</Abstract>
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