Doctoral Dissertations


Katrina Ward

Keywords and Phrases

Big data; Map-reduce; Mobile location; Privacy; Social media; Trajectory


"With the pervasive use of mobile devices, social media, home assistants, and smart devices, the idea of individual privacy is fading. More than ever, the public is giving up personal information in order to take advantage of what is now considered every day conveniences and ignoring the consequences. Even seemingly harmless information is making headlines for its unauthorized use (18). Among this data is user trajectory data which can be described as a user's location information over a time period (6). This data is generated whenever users access their devices to record their location, query the location of a point of interest, query directions to get to a location, request services to come to their location, and many other applications. This data could be used by a malicious adversary to track a user's movements, location, daily patterns, and learn details personal to the user. While the best course of action would be to hide this information entirely, this data can be used for many beneficial purposes as well. Emergency vehicles could be more efficiently routed based on trajectory patterns, businesses could make intelligent marketing or building decisions, and users themselves could benefit by taking advantage of more conveniences. There are several challenges to publishing this data while also preserving user privacy. For example, while location data has good utility, users expect their data to be private. For real world applications, users generate many terabytes of data every day. To process this volume of data for later use and anonymize it in order to hide individual user identities, this thesis presents an efficient algorithm to change the processing time for anonymization from days, as seen in (20), to a matter of minutes or hours. We cannot focus just on location data, however. Social media has a great many uses, one of which being the sharing of images. Privacy cannot stop with location, but must reach to other data as well. This thesis addresses the issue of image privacy in this work, as often images can be even more sensitive than location"--Abstract, page iv.


Lin, Dan

Committee Member(s)

Tauritz, Daniel R.
Leopold, Jennifer
Fu, Yanjie
Wunsch, Donald C.


Computer Science

Degree Name

Ph. D. in Computer Science


National Science Foundation (U.S.)
United States. Department of Education


This work was funded by the National Science Foundation (NSFDGE- 1433659, NSF-IIP-1332002), Department of Education (P200A120110).


Missouri University of Science and Technology

Publication Date

Spring 2019

Journal article titles appearing in thesis/dissertation

  • A parallel algorithm for anonymizing large-scale trajectory data
  • REMIND: Risk estimation mechanism for images in network distribution


xii, 107 pages

Note about bibliography

Includes bibliographic references.


© 2019 Katrina Johanna Ward, All rights reserved.

Document Type

Dissertation - Open Access

File Type




Thesis Number

T 11559

Electronic OCLC #