Trust Mechanism for Enforcing Compliance to Secondary Data Use Contracts
Bibliographic record
Abstract
In many research and business domains, there are efforts to develop systems that aggregate user data gathered by various data sources. This approach involves secondary sharing of user data and potentially benefits the user in terms of improved personalization and better experience. However, concerns regarding privacy arise when sharing user data with unknown third parties. These concerns can be alleviated at two stages: i) ensuring selective control of the applications to share user data with, and ii) monitoring and penalizing errant data consumers who violate the terms of their contractual agreement and potentially abuse user data. This paper addresses the second stage of data use contract enforcement. We propose a trust management mechanism for monitoring data consumers' compliance to the contractual agreements for which data was shared with them. The trust mechanism is based on user complaints about suspected privacy violations and is able to identify the data consumers who are responsible. The framework penalizes the data consumer found guilty of violating its data use agreement by decreasing its trust value. This makes the data consumer less likely to be selected to receive user data, and limits its participation in the user data marketplace, forcing it to pay a higher price for purchase of user data.
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 | 0.037 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".