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Usability heuristics for fast crime data anonymization in resource-constrained contexts
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...
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| Format: | Thesis |
| Language: | English |
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Department of Computer Science
2018
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| _version_ | 1867613183537053696 |
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| access_status_str | Open Access |
| author | Sakpere, Aderonke Busayo |
| author2 | Kayem, Anne |
| author_browse | Kayem, Anne Sakpere, Aderonke Busayo |
| author_facet | Kayem, Anne Sakpere, Aderonke Busayo |
| author_sort | Sakpere, Aderonke Busayo |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/28157 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:06.010Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | Department of Computer Science |
| publisherStr | Department of Computer Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/28157 Usability heuristics for fast crime data anonymization in resource-constrained contexts Sakpere, Aderonke Busayo Kayem, Anne Gain, James Crime Reporting Anonymization Sliding Window Resizing User Privacy Resource-Constrained Environment 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. 2018-05-25T07:50:09Z 2018-05-25T07:50:09Z 2018 Doctoral Thesis Doctoral PhD http://hdl.handle.net/11427/28157 eng application/pdf Department of Computer Science Faculty of Science University of Cape Town |
| spellingShingle | Crime Reporting Anonymization Sliding Window Resizing User Privacy Resource-Constrained Environment Sakpere, Aderonke Busayo Usability heuristics for fast crime data anonymization in resource-constrained contexts |
| thesis_degree_str | Doctoral |
| title | Usability heuristics for fast crime data anonymization in resource-constrained contexts |
| title_full | Usability heuristics for fast crime data anonymization in resource-constrained contexts |
| title_fullStr | Usability heuristics for fast crime data anonymization in resource-constrained contexts |
| title_full_unstemmed | Usability heuristics for fast crime data anonymization in resource-constrained contexts |
| title_short | Usability heuristics for fast crime data anonymization in resource-constrained contexts |
| title_sort | usability heuristics for fast crime data anonymization in resource constrained contexts |
| topic | Crime Reporting Anonymization Sliding Window Resizing User Privacy Resource-Constrained Environment |
| url | http://hdl.handle.net/11427/28157 |
| work_keys_str_mv | AT sakpereaderonkebusayo usabilityheuristicsforfastcrimedataanonymizationinresourceconstrainedcontexts |