Usability heuristics for fast crime data anonymization in resource-constrained contexts

Simple item page
dc.contributor.advisorKayem, Anneen_ZA
dc.contributor.advisorGain, Jamesen_ZA
dc.contributor.authorSakpere, Aderonke Busayoen_ZA
dc.date.accessioned2018-05-25T07:50:09Z
dc.date.available2018-05-25T07:50:09Z
dc.date.issued2018en_ZA
dc.description.abstractThis thesis considers the case of mobile crime-reporting systems that have emerged as an effective and efficient data collection method in low and middle-income countries. Analyzing the data, can be helpful in addressing crime. Since law enforcement agencies in resource-constrained context typically do not have the expertise to handle these tasks, a cost-effective strategy is to outsource the data analytics tasks to third-party service providers. However, because of the sensitivity of the data, it is expedient to consider the issue of privacy. More specifically, this thesis considers the issue of finding low-intensive computational solutions to protecting the data even from an "honest-but-curious" service provider, while at the same time generating datasets that can be queried efficiently and reliably. This thesis offers a three-pronged solution approach. Firstly, the creation of a mobile application to facilitate crime reporting in a usable, secure and privacy-preserving manner. The second step proposes a streaming data anonymization algorithm, which analyses reported data based on occurrence rate rather than at a preset time on a static repository. Finally, in the third step the concept of using privacy preferences in creating anonymized datasets was considered. By taking into account user preferences the efficiency of the anonymization process is improved upon, which is beneficial in enabling fast data anonymization. Results from the prototype implementation and usability tests indicate that having a usable and covet crime-reporting application encourages users to declare crime occurrences. Anonymizing streaming data contributes to faster crime resolution times, and user privacy preferences are helpful in relaxing privacy constraints, which makes for more usable data from the querying perspective. This research presents considerable evidence that the concept of a three-pronged solution to addressing the issue of anonymity during crime reporting in a resource-constrained environment is promising. This solution can further assist the law enforcement agencies to partner with third party in deriving useful crime pattern knowledge without infringing on users' privacy. In the future, this research can be extended to more than one low-income or middle-income countries.en_ZA
dc.identifier.apacitationSakpere, A. B. (2018). <i>Usability heuristics for fast crime data anonymization in resource-constrained contexts</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/28157en_ZA
dc.identifier.chicagocitationSakpere, Aderonke Busayo. <i>"Usability heuristics for fast crime data anonymization in resource-constrained contexts."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Computer Science, 2018. http://hdl.handle.net/11427/28157en_ZA
dc.identifier.citationSakpere, A. 2018. Usability heuristics for fast crime data anonymization in resource-constrained contexts. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Sakpere, Aderonke Busayo AB - This thesis considers the case of mobile crime-reporting systems that have emerged as an effective and efficient data collection method in low and middle-income countries. Analyzing the data, can be helpful in addressing crime. Since law enforcement agencies in resource-constrained context typically do not have the expertise to handle these tasks, a cost-effective strategy is to outsource the data analytics tasks to third-party service providers. However, because of the sensitivity of the data, it is expedient to consider the issue of privacy. More specifically, this thesis considers the issue of finding low-intensive computational solutions to protecting the data even from an "honest-but-curious" service provider, while at the same time generating datasets that can be queried efficiently and reliably. This thesis offers a three-pronged solution approach. Firstly, the creation of a mobile application to facilitate crime reporting in a usable, secure and privacy-preserving manner. The second step proposes a streaming data anonymization algorithm, which analyses reported data based on occurrence rate rather than at a preset time on a static repository. Finally, in the third step the concept of using privacy preferences in creating anonymized datasets was considered. By taking into account user preferences the efficiency of the anonymization process is improved upon, which is beneficial in enabling fast data anonymization. Results from the prototype implementation and usability tests indicate that having a usable and covet crime-reporting application encourages users to declare crime occurrences. Anonymizing streaming data contributes to faster crime resolution times, and user privacy preferences are helpful in relaxing privacy constraints, which makes for more usable data from the querying perspective. This research presents considerable evidence that the concept of a three-pronged solution to addressing the issue of anonymity during crime reporting in a resource-constrained environment is promising. This solution can further assist the law enforcement agencies to partner with third party in deriving useful crime pattern knowledge without infringing on users' privacy. In the future, this research can be extended to more than one low-income or middle-income countries. DA - 2018 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2018 T1 - Usability heuristics for fast crime data anonymization in resource-constrained contexts TI - Usability heuristics for fast crime data anonymization in resource-constrained contexts UR - http://hdl.handle.net/11427/28157 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/28157
dc.identifier.vancouvercitationSakpere AB. Usability heuristics for fast crime data anonymization in resource-constrained contexts. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Computer Science, 2018 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/28157en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Computer Scienceen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherCrime Reportingen_ZA
dc.subject.otherAnonymizationen_ZA
dc.subject.otherSliding Window Resizingen_ZA
dc.subject.otherUser Privacyen_ZA
dc.subject.otherResource-Constrained Environmenten_ZA
dc.titleUsability heuristics for fast crime data anonymization in resource-constrained contextsen_ZA
dc.typeDoctoral Thesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnamePhDen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
thesis_sci_2018_sakpere_aderonke_busayo.pdf
Size:
6.81 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
PhD / Doctoral