Bibliographic record
Abstract
Abstract. In today’s society, people have very little control over what kinds of personal data are collected and stored by various agencies in both the private and public sectors. We describe an approach to addressing this problem that allows individuals to specify constraints on the way their own data is used. Our solution uses formal methods to allow developers of software that processes personal data to provide assurances that the software meets the specified privacy constraints. In the domain of privacy, it is often not sufficient to express properties of interest as a relation between the input and output of a program as is done for general program correctness. Here we consider a stronger class of properties that allows us to express constraints on information flow. In particular, we can express that an algorithm does not leak any information from particular “sensitive ” values. We describe a general methodology for expressing this kind of information flow property as Hoare-style program verification judgments. We begin with the Java Modelling Language (JML), which is a behavioral interface specification language designed for Java, and we extend the language to include new concepts and keywords for expressing such properties. We use the Krakatoa tool which starts from JMLannotated Java programs, generates proof obligations in the Coq Proof Assistant, and helps to automate their proofs. We extend the Krakatoa tool to understand our extensions to JML and to generate the new form of required proof obligations. We illustrate our method on several data mining algorithms implemented in Java. 1
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".