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Automating user privacy policy recommendations in social media
Most Social Media Platforms (SMPs) implement privacy policies that enable users to protect their sensitive information against privacy violations. However, observations indicate that users find these privacy policies cumbersome and difficult to configure. Consequently, various approaches have been p...
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| Format: | Thesis |
| Language: | English |
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Department of Computer Science
2016
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| _version_ | 1867613212140109824 |
|---|---|
| access_status_str | Open Access |
| author | Abuelgasim, Ammar |
| author2 | Kayem, Anne |
| author_browse | Abuelgasim, Ammar Kayem, Anne |
| author_facet | Kayem, Anne Abuelgasim, Ammar |
| author_sort | Abuelgasim, Ammar |
| collection | Thesis |
| description | Most Social Media Platforms (SMPs) implement privacy policies that enable users to protect their sensitive information against privacy violations. However, observations indicate that users find these privacy policies cumbersome and difficult to configure. Consequently, various approaches have been proposed to assist users with privacy policy configuration. These approaches are however, limited to either protecting only profile attributes, or only protecting user-generated content. This is problematic, because both profile attributes and user-generated content can contain sensitive information. Therefore, protecting one without the other, can still result in privacy violations. A further drawback of existing approaches is that most require considerable user input which is time consuming and inefficient in terms of privacy policy configuration. In order to address these problems, we propose an automated privacy policy recommender system. The system relies on the expertise of existing social media users, as well as the user's privacy policy history in order to provide him/her with personalized privacy policy suggestions for both profile attributes, and user-generated content. Results from our prototype implementation indicate that the proposed recommender system provides accurate privacy policy suggestions, with minimum user input. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/20969 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:33.381Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| 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:11427/20969 Automating user privacy policy recommendations in social media Abuelgasim, Ammar Kayem, Anne Computer Science Most Social Media Platforms (SMPs) implement privacy policies that enable users to protect their sensitive information against privacy violations. However, observations indicate that users find these privacy policies cumbersome and difficult to configure. Consequently, various approaches have been proposed to assist users with privacy policy configuration. These approaches are however, limited to either protecting only profile attributes, or only protecting user-generated content. This is problematic, because both profile attributes and user-generated content can contain sensitive information. Therefore, protecting one without the other, can still result in privacy violations. A further drawback of existing approaches is that most require considerable user input which is time consuming and inefficient in terms of privacy policy configuration. In order to address these problems, we propose an automated privacy policy recommender system. The system relies on the expertise of existing social media users, as well as the user's privacy policy history in order to provide him/her with personalized privacy policy suggestions for both profile attributes, and user-generated content. Results from our prototype implementation indicate that the proposed recommender system provides accurate privacy policy suggestions, with minimum user input. 2016-07-28T12:23:08Z 2016-07-28T12:23:08Z 2016 Master Thesis Masters MSc http://hdl.handle.net/11427/20969 eng application/pdf Department of Computer Science Faculty of Science University of Cape Town |
| spellingShingle | Computer Science Abuelgasim, Ammar Automating user privacy policy recommendations in social media |
| thesis_degree_str | Master's |
| title | Automating user privacy policy recommendations in social media |
| title_full | Automating user privacy policy recommendations in social media |
| title_fullStr | Automating user privacy policy recommendations in social media |
| title_full_unstemmed | Automating user privacy policy recommendations in social media |
| title_short | Automating user privacy policy recommendations in social media |
| title_sort | automating user privacy policy recommendations in social media |
| topic | Computer Science |
| url | http://hdl.handle.net/11427/20969 |
| work_keys_str_mv | AT abuelgasimammar automatinguserprivacypolicyrecommendationsinsocialmedia |