Joint Privacy and Security of Multiple Biometric Systems
Bibliographic record
Abstract
This paper explores the design of biometric authentication in the context of a single user that has enrolled in multiple (distinct) authentication systems. The compromise of some subset of these systems will generally impact both the privacy of the user's biometric information and the security of the balance of the systems. In this work we consider how to design the systems jointly to minimize losses in privacy and security in the case of such compromise. It turns out that there is a tension between the two objectives, resulting in a privacy/security tradeoff.We introduce worst-case privacy and security measures, and consider the tradeoff between them, in the context of the “secure sketch” architecture. Secure sketch systems are based on error correction codes, and the considerations of joint design that we pose result in a novel code design problem. We first study the design problem algebraically and identify an equivalence with a type of subspace packing problem. While the packing problem fully characterizes the design space, it does not yield an explicit characterization. We then turn to a “fixed-basis” subspace of the general design space. We map a relaxed version of the fixed-basis design problem to a linear program which, after exploiting much symmetry, leads to an explicit tradeoff between security and privacy. While we show that fixed-basis designs are restrictive in terms of the achievable privacy/security tradeoffs, they have the advantage of being easily mapped to existing codes (e.g., low-density parity check codes), and thence to immediate deployment. Finally, we conjecture that the achievable privacy/security tradeoff of fixed-basis designs is characterized by an extremely simple analytic expression, one that matches our numerical results.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".