South African Electoral Commission’s mobile app for voters: Data privacy and security dimensions
Bibliographic record
Abstract
In 2014, the Electoral Commission of South Africa (also known the “IEC”) launched a mobile app to support voter participation in electoral processes. The app, called IEC South Africa, can be used to verify, update, and confirm a voter’s registration details and voting station. It also provides an interface for special-vote applications and real-time election results. This study conducted a privacy and security analysis of the app, through a compliance review of the IEC’s privacy policy in terms of the South African data protection legislation, followed by an analysis of the app’s APK files, permissions, third-party trackers, and vulnerabilities, including API (application programming interface) calls. The analysis revealed several security and privacy concerns, including inadequately secured API keys, the potential for unauthorised access, and the potential for data breaches. In addition, the presence of advertising and analytics trackers suggested third-party data-sharing, raising concerns about transparency and user consent. The study draws attention to the need for the IEC to take action to address the app’s security and privacy weaknesses. The study also demonstrates the importance of data minimisation, transparent practices, and adherence to privacy policies in order to maintain user trust and security in electoral technology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".