Sharing Policies in Multiuser Privacy Scenarios: Incorporating Context, Preferences, and Arguments in Decision Making

Ricard L. Fogues, Pradeep K. Murukannaiah, Jose Such, Munindar P. Singh

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)
298 Downloads (Pure)


Social network services (SNSs) enable users to conveniently share personal information. Often, the information shared concerns other people, especially other members of the SNS. In such situations, two or more people can have conflicting privacy preferences; thus, an appropriate sharing policy may not be apparent. We identify such situations as multiuser privacy scenarios. Current approaches propose finding a sharing policy through preference aggregation. However, studies suggest that users feel more confident in their decisions regarding sharing when they know the reasons behind each other’s preferences. The goals of this paper are (1) understanding how people decide the appropriate sharing policy in multiuser scenarios where arguments are employed, and (2) developing a computational model to predict an appropriate sharing policy for a given scenario. We report on a study that involved a survey of 988 Amazon Mechanical Turk (MTurk) users about a variety of multiuser scenarios and the optimal sharing policy for each scenario. Our evaluation of the participants’ responses reveals that contextual factors, user preferences, and arguments influence the optimal sharing policy in a multiuser scenario. We develop and evaluate an inference model that predicts the optimal sharing policy given the three types of features. We analyze the predictions of our inference model to uncover potential scenario types that lead to incorrect predictions, and to enhance our understanding of when multiuser scenarios are more or less prone to dispute.
Original languageEnglish
Article number5
Pages (from-to)1-29
Number of pages29
JournalACM Transactions on Computer-Human Interaction
Issue number1
Early online date22 Mar 2017
Publication statusPublished - Mar 2017


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