Innocent until proven guilty: Privacy-preserving search over a central CODIS criminal database from the field
Bibliographic record
Abstract
Abstract The presumption of innocence (i.e., the principle that one is considered innocent until proven guilty) is a cornerstone of the criminal justice system in many countries, including the United States. DNA analysis is an important tool for criminal investigations 1 . In the U.S. alone, it has already aided in over half a million investigations using the Combined DNA Index System (CODIS) and associated DNA databases 2 . CODIS includes DNA profiles of crime scene forensic samples, convicted offenders, missing persons and more. The CODIS framework is currently used by over 50 other countries 3 including much of Europe, Canada, China and more. During investigations, DNA samples can be collected from multiple individuals who may have had access to, or were found near a crime scene, in the hope of finding a single criminal match 4 . Controversially, CODIS samples are sometimes retained from adults and juveniles despite not yielding any database match 4–6 . Here we introduce a cryptographic algorithm that finds any and all matches of a person’s DNA profile against a CODIS database without revealing anything about the person’s profile to the database provider. With our protocol, matches are immediately identified as before; however, individuals who do not match anything in the database retain their full privacy. Our novel algorithm runs in 40 seconds on a CODIS database of 1,000,000 entries, enabling its use to privately screen potentially-innocent suspects even in the field.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".