GenoDroid: Are Privacy-Preserving Genomic Tests Ready for Prime Time?
Bibliographic record
Abstract
As fast and accurate sequencing of human genomes becomes affordable, it is expected that individuals will soon be able to carry around copies of their sequenced DNA, using it for medical, identification, and social purposes. This will undoubtedly prompt a wide range of new and interesting genomic applications. However, the very same progress raises some worrisome privacy issues, since a genome represents a treasure trove of highly personal and sensitive information. Some recent research explored privacypreserving personal genomic operations by applying (or customizing) cryptographic protocols based on techniques such as: conditional oblivious transfer, garbled circuits, and homomorphic encryption. In this paper, we take this line of work a step further by investigating real-world practicality and usability of (as well as interest in) some of these methods. Motivated by both medical and social applications, we aim to test viability of privacy-agile computational genomic tests in a portable and pervasive setting of modern smartphones. We design a personal genomic toolkit (called GenoDroid), implement it on the Android platform, assess its performance, and conduct a pilot usability study that yields some interesting results. Copyright 2012 ACM.
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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".