Privacy and the market for private data: A negotiation model to capitalize on private data
Bibliographic record
Abstract
The market for consumer information is already a lively market, where consumer information and consumer profile data are often among the most valuable assets owned by online retailers. The value of such commodity derives from the ability of firms to identify consumers and charge them personalized prices flj. We argue that if consumers' identity and personal information is such a valuable asset, should not consumers benefit from their asset as well? In this paper, we propose a negotiation process between an online consumer agent and an online seller. The online consumer agent acts on behalf of consumers to maximize their social welfare. In our model, the agent derives a quantified privacy risk for each private data and uses it to determine a cost premium value to make the bargaining process manageable. We also provide a computational example to evaluate the model.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".