Paper Number
2689
Paper Type
Completed
Description
Understanding consumers’ privacy preferences is crucial for firms and policymakers to establish trust and encourage innovation and competition. With the widespread use of digital technologies, individuals generate and share vast amounts of data about themselves in the public domain. Even without knowing a person’s private information, the psychosocial traits revealed in public data can provide valuable insights into their privacy preferences. In this study, we aim to predict personalized privacy preferences using social media posts. Our prediction model shows that psychosocial traits such as personality, lifestyles, risk preference, economic thinking, emotions, etc extracted from posts provide significantly greater predictive power than demographic characteristics. Furthermore, we demonstrate the practical value and impact of our model for business and society through a simulation analysis. Our tool can help platforms and policymakers estimate the impact of privacy policies and prevent potential harms such as discrimination.
Recommended Citation
Wang, Wen and Li, Beibei, "Learning Personalized Privacy Preference From Public Data" (2023). ICIS 2023 Proceedings. 16.
https://aisel.aisnet.org/icis2023/cyber_security/cyber_security/16
Learning Personalized Privacy Preference From Public Data
Understanding consumers’ privacy preferences is crucial for firms and policymakers to establish trust and encourage innovation and competition. With the widespread use of digital technologies, individuals generate and share vast amounts of data about themselves in the public domain. Even without knowing a person’s private information, the psychosocial traits revealed in public data can provide valuable insights into their privacy preferences. In this study, we aim to predict personalized privacy preferences using social media posts. Our prediction model shows that psychosocial traits such as personality, lifestyles, risk preference, economic thinking, emotions, etc extracted from posts provide significantly greater predictive power than demographic characteristics. Furthermore, we demonstrate the practical value and impact of our model for business and society through a simulation analysis. Our tool can help platforms and policymakers estimate the impact of privacy policies and prevent potential harms such as discrimination.
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