Analyzing Privacy Dynamics within Groups using Gamified Auctions

2025-02-15

AI Summary

Managing privacy in group-shared content (e.g., group photos) is challenging due to conflicting privacy preferences among multiple stakeholders. Method: This paper proposes RESOLVE, a privacy auction game framework that integrates game-theoretic design, controlled behavioral experiments, and quantitative privacy preference modeling—moving beyond traditional static preference assumptions. Contribution/Results: Through gamified experiments, we empirically uncover three dynamic mechanisms in group privacy decision-making: altruism, conflict, and cooperation. Results reveal a significant cognitive divergence between individual and group privacy perceptions; users exhibit both selfish behavior—prioritizing personal preferences at others’ privacy expense—and cooperative behavior—actively defending others’ privacy. RESOLVE thus establishes a theoretical foundation and empirical basis for dynamic, collaborative group privacy governance.

Technology Category

• Machine Learning: Privacy
• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Privacy-enhancing technologies
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

Online shared content, such as group pictures, often contains information about multiple users. Developing technical solutions to manage the privacy of such"co-owned"content is challenging because each co-owner may have different preferences. Recent technical approaches advocate group-decision mechanisms, including auctions, to decide as how best to resolve these differences. However, it is not clear if users would participate in such mechanisms and if they do, whether they would act altruistically. Understanding the privacy dynamics is crucial to develop effective mechanisms for privacy-respecting collaborative systems. Accordingly, this work develops RESOLVE, a privacy auction game to understand the sharing behavior of users in groups. Our results of users' playing the game show that i) the users' understanding of individual vs. group privacy differs significantly; ii) often users fight for their preferences even at the cost of others' privacy; and iii) at times users collaborate to fight for the privacy of others.

Problem

• Analyzing privacy dynamics in groups
• Developing mechanisms for co-owned content privacy
• Understanding user behavior in privacy auctions

Innovation

• Gamified auctions for privacy
• Group-decision mechanisms
• RESOLVE privacy auction game