An explainable assistant for multiuser privacy

Francesca Mosca*, Jose Such

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

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.

Original languageEnglish
Article number10
JournalAutonomous Agents and Multi-Agent Systems
Volume36
Issue number1
Early online date15 Jan 2022
DOIs
Publication statusPublished - Apr 2022

Keywords

  • Agent-based simulations
  • Explainable agent
  • Multiuser privacy
  • User study

Fingerprint

Dive into the research topics of 'An explainable assistant for multiuser privacy'. Together they form a unique fingerprint.

Cite this