An explainable assistant for multiuser privacy

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Abstract

Multiuser Privacy (MP) concerns the protection of personal information in situations where such information is co-owned by multiple users. MP is particularly problematic in collaborative platforms such as online social networks (OSN). In fact, too often OSN users experience privacy violations due to conflicts generated by other users sharing content that involves them without their permission. Previous studies show that in most cases MP
conflicts could be avoided, and are mainly due to the difficulty for the uploader to select appropriate sharing policies. For this reason, we present ELVIRA, the first fully explainable personal assistant that collaborates with other ELVIRA agents to identify the optimal sharing policy for a collectively owned content. An extensive evaluation of this agent through software simulations and two user studies suggests that ELVIRA, thanks to its
properties of being role-agnostic, adaptive, explainable and both utility- and value-driven, would be more successful at supporting MP than other approaches presented in the literature in terms of (i) trade-of between generated utility and promotion of moral values, and (ii) users’ satisfaction of the explained recommended output.
Original languageEnglish
Pages (from-to)1-45
JournalAutonomous Agents and Multi-Agent Systems
Volume36
Issue number10
Publication statusPublished - 15 Jan 2022

Keywords

  • Explainable Agents
  • Multiuser Privacy
  • Agent-based Simulations
  • User Study

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