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An explainable assistant for multiuser privacy

Research output: Contribution to journalArticlepeer-review

Francesca Mosca, Jose Such

Original languageEnglish
Article number10
JournalAutonomous Agents and Multi-Agent Systems
Volume36
Issue number1
Early online date15 Jan 2022
DOIs
E-pub ahead of print15 Jan 2022
PublishedApr 2022

Bibliographical note

Funding Information: The authors are grateful to the Science Officer of the Doping Control Center, USM Penang for the analysis of 3-MCPD and to the Ministry of Science, Technology and Innovation, Malaysia (304/PTEKIND /640042/K105) for funding this research. Publisher Copyright: © 2022, The Author(s).

King's Authors

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-off between generated utility and promotion of moral values, and (ii) users’ satisfaction of the explained recommended output.

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