Aftermath of bustamante attack on genomic beacon service
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: With the enormous need for federated eco-system for holding global genomic and clinical data, Global Alliance for Genomic and Health (GA4GH) has created an international website called beacon service which allows a researcher to find out whether a specific dataset can be utilized to his or her research beforehand. This simple webservice is quite useful as it allows queries like whether a certain position of a target chromosome has a specific nucleotide. However, the increased integration of individuals genomic data into clinical practice and research raised serious privacy concern. Though the answer of such queries are yes or no in Bacon network, it results in serious privacy implication as demonstrated in a recent work from Shringarpure and Bustamante. In their attack model, the authors demonstrated that with a limited number of queries, presence of an individual in any dataset can be determined. METHODS: We propose two lightweight algorithms (based on randomized response) which captures the efficacy while preserving the privacy of the participants in a genomic beacon service. We also elaborate the strength and weakness of the attack by explaining some of their statistical and mathematical models using real world genomic database. We extend their experimental simulations for different adversarial assumptions and parameters. RESULTS: We experimentally evaluated the solutions on the original attack model with different parameters for better understanding of the privacy and utility tradeoffs provided by these two methods. Also, the statistical analysis further elaborates the different aspects of the prior attack which leads to a better risk management for the participants in a beacon service. CONCLUSIONS: The differentially private and lightweight solutions discussed here will make the attack much difficult to succeed while maintaining the fundamental motivation of beacon database network.
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.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.050 | 0.072 |
| 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 it