Transferring recommendations through privacy user models across domains

Abstract

Although privacy settings are important not only for data privacy, but also to prevent hacking attacks like social engineering that depend on leaked private data, most users do not care about them. Research has tried to help users in setting their privacy settings by using some settings that have already been adapted by the user or individual factors like personality to predict the remaining settings. But in some cases, neither is available. However, the user might have already done privacy settings in another domain, for example, she already adapted the privacy settings on the smartphone, but not on her social network account. In this article, we investigate with the example of four domains (social network posts, location sharing, smartphone app permission settings and data of an intelligent retail store), whether and how precise privacy settings of a domain can be predicted across domains. We performed an exploratory study to examine which privacy settings of the aforementioned domains could be useful, and validated our findings in a validation study. Our results indicate that such an approach works with a prediction precision about 15%–20% better than random and a prediction without input coefficients. We identified clusters of domains that allow model transfer between their members, and discuss which kind of privacy settings (general or context-based) leads to a better prediction accuracy. Based on the results, we would like to conduct user studies to find out whether the prediction precision is perceived by users as a significant improvement over a “one-size-fits-all” solution, where every user is given the same privacy settings.

A Design Theory for Transparency of Information Privacy Practices

“…Nevertheless, conversational agents present an interesting option in cases where voice-based user interfaces are employed in the IS anyway. Similar to the transfer of consumers' privacy settings between domains (eg, Raber andKrüger 2022, Shanmugarasa et al 2022), consumers' information needs could be inferred based on information needs they have already exhibited in similar IS and contexts. However, unresolved challenges include how to avoid the introduction of additional privacy risks and concerns resulting from the necessary exchanges of user models across different IS and how to detect and account for differences between contexts (Raber and Krüger 2022).…”

“…Similar to the transfer of consumers' privacy settings between domains (eg, Raber andKrüger 2022, Shanmugarasa et al 2022), consumers' information needs could be inferred based on information needs they have already exhibited in similar IS and contexts. However, unresolved challenges include how to avoid the introduction of additional privacy risks and concerns resulting from the necessary exchanges of user models across different IS and how to detect and account for differences between contexts (Raber and Krüger 2022). As discussed by Rubinstein and Good (2013), a more effortful but also more thorough approach would be to leverage user experience design methods to better understand consumers' information needs and adapt transparency artifacts accordingly.…”