Affiliation:
1. Pennsylvania State University, PA, USA
Abstract
In this article, we aim to answer an important set of questions about the potential longitudinal effects of repeated sharing and privacy settings decisions over jointly managed content among users in a social network.
We model user interactions through a repeated game in a network graph. We present a variation of the one-shot Ultimatum Game, wherein individuals interact with peers to make a decision on a piece of shared content. The outcome of this game is either success or failure, wherein success implies that a satisfactory decision for all parties is made and failure instead implies that the parties could not reach an agreement. Our proposed game is grounded in empirical data about individual decisions in repeated pairwise negotiations about jointly managed content in a social network. We consider both a “continuous” privacy model as well the “discrete” case of a model wherein privacy values are to be chosen among a fixed set of options. We formally demonstrate that over time, the system converges toward a “fair” state, wherein each individual’s preferences are accounted for. Our discrete model is validated by way of a user study, where participants are asked to propose privacy settings for own shared content from a small, discrete set of options.
Funder
National Science Foundation
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications
Cited by
4 articles.
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1. A Bargaining-Game Framework for Multi-Party Access Control;Proceedings of the 29th ACM Symposium on Access Control Models and Technologies;2024-06-24
2. On the Emergence of Fairness in the Evolutionary Dictator Game with Edge Weight Learning;2023 31st Irish Conference on Artificial Intelligence and Cognitive Science (AICS);2023-12-07
3. A Psychometric Scale to Measure Individuals’ Value of Other People’s Privacy (VOPP);Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems;2023-04-19
4. On the Potential of Mediation Chatbots for Mitigating Multiparty Privacy Conflicts - A Wizard-of-Oz Study;Proceedings of the ACM on Human-Computer Interaction;2023-04-14