A Multi-Party Agent for Privacy Preference Elicitation
Keywords:multi-party, privacy-preserving, eliciting, classification, Rasch model, cold start
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.
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