The privacy/security tradeoff across jointly designed linear authentication systems
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
In the area of secure biometrics, work has been done to build an information theoretic framework characterizing privacy and security of single biometric systems. People have worked extensively on designing such systems, some cryptographic in nature, and others tied to error correcting codes. However, there is still little known about security and privacy across multiple jointly designed systems. This work will focus on the privacy/security tradeoff across multiple “secure sketch” biometric systems. Secure sketch is a type of biometric system architecture related to error-correcting codes where a system is characterized by a parity-check matrix over a finite field, or equivalently by a subspace of a vector space over that same field. Given a set of systems (a design), we introduce worst-case measures of privacy leakage and security in the case that a subset of the systems becomes compromised. It turns out that more secure designs are necessarily less private and vice versa. We study the tradeoff between privacy and security by relaxing a restricted version of the problem, by studying the algebraic structure of the problem, and by formulating graph theoretic questions. These approaches generate bounds on achievable privacy/security pairs.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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