A Multi-Party Agent for Privacy Preference Elicitation
Bibliographic record
Abstract
In today’s world, the decisions that individuals make online often include their surroundings and social circles. For example, Alice posts on TikTok to celebrate her friend Bob’s birthday and reminisce about their best memories together. She, then, proceeds to create a campaign to fund her local place of worship and tags members of her community who share her religious belief. Alice might equally like to take initiative at work as she plans her team-building trip and excitedly shares the programme on Facebook. While doing all of this, she is involving family members, close friends, co-workers, acquaintances, and others from her social circle, all of whom might have different opinions about their privacy. While she sees no issue with her actions, her friend Bob, for one, might not agree, hence, the issue of multi-party privacy. Many researchers have focused on conflict resolution, which occurs when the sharer’s privacy preferences do not align with the other parties involved. However, one key point in this approach is eliciting the preferences of these individuals. Oftentimes, there is an underlying assumption that the system has sufficient historical data to represent the perspective of the multi-party members. The problem is that this is not always the case in real life and the cold start problem might be unavoidable. The system that is meant to nudge the sharer to reduce the multi-party disclosure might not even be capable of representing the preferences of everyone involved at the beginning. Hence, this paper addresses this issue through the use of the Classification and Regression Tree (CART) combined with the Rasch model. Study participants (N = 800) responded to realistic scenarios showcasing multi-party disclosure, which is used to construct and test the multi-party agent. The results suggest that the system performs well in overcoming the cold start problem as reported by the accuracy, precision, and recall. Received: 5 November 2022 | Revised: 23 December 2022 | Accepted: 26 December 2022 Conflicts of Interest The authors declare that they have no conflicts of interest to this work.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".