My Genome Belongs to Me: Controlling Third Party Computation on Genomic Data
Bibliographic record
Abstract
Abstract An individual’s genetic information is possibly the most valuable personal information. While knowledge of a person’s DNA sequence can facilitate the diagnosis of several heritable diseases and allow personalized treatment, its exposure comes with significant threats to the patient’s privacy. Currently known solutions for privacy-respecting computation require the owner of the DNA to either be heavily involved in the execution of a cryptographic protocol or to completely outsource the access control to a third party. This motivates the demand for cryptographic protocols which enable computation over encrypted genomic data while keeping the owner of the genome in full control. We envision a scenario where data owners can exercise arbitrary and dynamic access policies, depending on the intended use of the analysis results and on the credentials of who is conducting the analysis. At the same time, data owners are not required to maintain a local copy of their entire genetic data and do not need to exhaust their computational resources in an expensive cryptographic protocol. In this work, we present METIS, a system that assists the computation over encrypted data stored in the cloud while leaving the decision on admissible computations to the data owner. It is based on garbled circuits and supports any polynomially-computable function. A critical feature of our system is that the data owner is free from computational overload and her communication complexity is independent of the size of the input data and only linear in the size of the circuit’s output. We demonstrate the practicality of our approach with an implementation and an evaluation of several functions over real datasets.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".