Revisiting VERTIGO and VERTIGO-CI: Identifying confidentiality breaches and introducing a statistically sound, efficient alternative
Bibliographic record
Abstract
Abstract Background Health Data Research Network Canada is tasked with facilitating large-scale health data research, such as statistical analyses that integrate, within a single model, data collected by different organizations, each holding distinct subsets of features corresponding to the same individuals, thereby forming a vertical data partition. To support logistic regression analyses in this setting, we assessed two recently proposed algorithms, VERTIGO and VERTIGO-CI, which enable parameter estimation and confidence interval computation, respectively, with respect to three aspects: the risk of re-identifying patient feature data, communication efficiency, and the extent to which model interpretability is preserved. This study has three main objectives: (1) highlighting confidentiality issues that arise with VERTIGO-CI, as well as those that may occur with VER-TIGO when a data node holds only binary covariates; (2) reducing the number of required communication rounds; and (3) proposing an alternative (RidgeLog-V) to VERTIGO that excludes the intercept from the penalty term, which VER-TIGO otherwise includes. Methods We inspected the quantities exchanged in the original algorithms and used linear algebra to identify reverse-engineering procedures that the coordinating center could employ to reconstruct raw data. We also analyzed the objective function of the optimization problem, leading to the proposal of an alternative formulation that requires only a single round of communication while allowing the intercept to be excluded from the penalty term. Results We showed that, when the VERTIGO-CI algorithm is executed, the coordinating center can reconstruct all individual-level data using simple vectormatrix operations. When the VERTIGO algorithm is executed and a data node has binary covariates only, the coordinating center may be able to recover individual data when parameter estimates are shared. We adapted the VERTIGO algorithm to reduce the number of communications and proposed a variant that excludes the intercept from the penalty term. Conclusions While the use of VERTIGO-CI, or of VERTIGO with binary covariates does not involve directly sharing raw data, confidentiality breaches may arise through reverse-engineering, illustrating that that the distributed nature of an algorithm does not inherently guarantee data privacy. This work also proposed a new algorithm (RidgeLog-V) that reduces operational costs and enhances model interpretability.
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 | 0.050 | 0.197 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.004 | 0.011 |
| 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".