Leading Johnny to Water: Designing for Usability and Trust.
Bibliographic record
Abstract
Although the means and the motivation for securing private messages and emails with strong end-to-end encryption exist, we have yet to see the widespread adoption of existing implementations. Previous studies have suggested that this is due to the lack of usability and understanding of existing systems such as PGP. A recent study by Ruoti et al. suggested that transparent, standalone encryption software that shows ciphertext and allows users to manually participate in the encryption process is more trustworthy than integrated, opaque software and just as usable. In this work, we critically examine this suggestion by revisiting their study, deliberately investigating the effect of integration and transparency on users’ trust. We also implement systems that adhere to the OpenPGP standard and use end-to-end encryption without reliance on third-party key escrow servers. We find that while approximately a third of users do in fact trust standalone encryption applications more than browser extensions that integrate into their webmail client, it is not due to being able to see and interact with ciphertext. Rather, we find that users hold a belief that desktop applications are less likely to transmit their personal messages back to the developer of the software. We also find that despite this trust difference, users still overwhelmingly prefer integrated encryption software, due to the enhanced user experience it provides. Finally, we provide a set of design principles to guide the development of future consumerfriendly end-to-end encryption tools.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".