Visualizing Differential Privacy: Assessing Infographics' Impact on Layperson Data-sharing Decisions and Comprehension
Bibliographic record
Abstract
Differential privacy (DP) has emerged as a promising approach for protecting users' data in the era of big data and machine learning. Despite its deployment by governments and or-ganizations, the concept of DP remains difficult for non-technical users to comprehend. Visual aids, such as infographics, have the potential to bridge this knowledge gap and enable users to make informed data-sharing decisions. In this paper, we propose to use carefully designed infographics to explain DP and compare their effectiveness with traditional text descriptions. We conducted a vignette survey study with 367 participants on Prolific and found that our static and dynamic infographic designs improved participants' understanding of DP, including its mechanism and implication compared with text descriptions. Our infographics also enhance users' understanding of DP and educate them on whether the privacy budget ∊is exposed when sharing their highly sensitive information. This research contributes to the growing body of literature on designing effective DP descriptions to communicate DP to laypeople to facilitate their data-sharing decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.002 | 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".