On the Memorability of System-generated PINs: Can Chunking Help?
Bibliographic record
Abstract
To ensure that users do not choose weak personal identification numbers (PINs), many banks give out systemgenerated random PINs. 4-digit is the most commonly used PIN length, but 6-digit system-generated PINs are also becoming popular. The increased security we get from using system-generated PINs, however, comes at the cost of memorability. And while banks are increasingly adopting systemgenerated PINs, the impact on memorability of such PINs has not been studied. We conducted a large-scale online user study with 9,114 participants to investigate the impact of increased PIN length on the memorability of PINs, and whether number chunking 1 techniques (breaking a single number into multiple smaller numbers) can be applied to improve memorability for larger PIN lengths. As one would expect, our study shows that system-generated 4-digit PINs outperform 6-, 7-, and 8-digit PINs in long-term memorability. Interestingly, however, we find that there is no statistically significant difference in memorability between 6-, 7-, and 8-digit PINs, indicating that 7-, and 8-digit PINs should also be considered when looking to increase PIN length to 6-digits from currently common length of 4-digits for improved security. By grouping all 6-, 7-, and 8-digit chunked PINs together, and comparing them against a group of all non-chunked PINs, we find that chunking, overall, improves memorability of system-generated PINs. To our surprise, however, none of the individual chunking policies (e.g., 0000-00-00) showed statistically significant improvement over their peer non� Part of this work was done while Dr. Huh and Dr. Bobba were at the University of Illinois. 1 Note that our notion of chunking differs from the traditional notion in that we do not chunk numbers into semantically meaningful pieces.
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 | 0.004 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".