EAGER: DCL: SaTC: Enabling Interdisciplinary Collaboration: Adapting Economic Games to Personalize Privacy and Security Nudges

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

Modern social communication systems, ranging from email to social media systems, present a dizzying number of decisions for their users. Moreover, privacy and security configurations are often hidden and opaque. Thus it is hard for individuals to manage configurations and behaviors in ways that are consistent with their preferences. Bad actors or adversarial agents can take advantage of ambiguity or information leaks that result from poor settings and user uncertainty to find attack routes for disinformation and phishing. Conversely, socially beneficial behaviors that require data sharing are also hindered. A better understanding of the relationships among preferences, behaviors, and interfaces can help address these concerns. Business interests in preventing phishing can be preserved by understanding individual preferences and their relationship to employee behaviors. Personalized interventions can be applied when preferences conflict with socially beneficial data behaviors. This project synthesizes insights from behavioral economics and computing to promote information security. The project team seeks to tackle this challenge by modeling individual preferences through the use of decision- and game- theoretic economic games to identify individual risk, ambiguity, and information preferences. The experimental games simulate competing and cooperating incentives and strategies, such as in the Prisoner’s Dilemma game. The appeal of these types of games is that a comparatively small subset of them may be useful to model a set of preferences that are predictive of a broad range of real-world behaviors. The team is modifying these games to better align with real-world communication tasks in a social media system. Behavioral experiments can provide novel evidence of the predictive value of the games and validate their use in novel contexts. The results may transform work in human-computer interaction, security, and privacy research and design by isolating simpler incentive-compatible instruments that model user preferences and correlate well with behaviors. This connection enables novel interventions from personalized interfaces to personally or socially beneficial interventions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.