Negotiating Exchange of Private Information for Web Service Eligibility
Bibliographic record
Abstract
Private information about individuals that engage in e-commerce business transactions is of economic value to businesses for market analysis and for identifying possible future partners. For various reasons, maintaining the privacy of that information is important to these individuals, including avoiding unwelcome communication, spam, from those businesses or their associates. In this paper we advocate a negotiation strategy to be used by an individual deciding whether or not to divulge information to a specific electronic business for a specific purpose, such as achieving preferential status with a service provider or a discounted price from a vendor. The strategy makes use of explanation techniques for expert systems that answer 'how', 'what if' and 'why not' questions. We assume that the business practices of the provider or vendor are available as explicit business rules, including the eligibility criteria for preferential status and price discounts. Our prototype allows the user to obtain a proof that the information to be given is both necessary and sufficient for achieving the eligibility / discount - answering 'how' eligibility is established. The communication protocol with the prototype also includes 'what if' dialogues allowing a user to assess the difficulty and benefits of achieving eligibility, and 'why not' dialogues for identifying missing eligibility criteria. The prototype is built upon the emerging standard Web Services architecture. Thus the prototype allows a business to expose its business practices, educating its customers, so it can provide the most appropriate service for a given individual. The prototype engages the customer to assess the benefit of exposing some private information to the business. Through the 'what if' interface, the customer can be aware of the complete set of information that will be necessary to achieve the desired eligibility before any private information is actually transmitted. We offer an example where a user is negotiating a car price discount.
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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 | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".