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ELVIRA: an Explainable Agent for Value and Utility-driven Multiuser Privacy

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Original languageEnglish
Title of host publicationInternational Conference on Autonomous Agents and Multiagent Systems (AAMAS)
Accepted/In press2021


  • aamas2021_cr

    aamas2021_cr.pdf, 717 KB, application/pdf

    Uploaded date:29 Jan 2021

    Version:Accepted author manuscript

    Licence:CC BY

King's Authors


Online social networks fail to support users to adequately share co-owned content, which leads to privacy violations.
Scholars proposed collaborative mechanisms to support users, but they did not satisfy one or more requirements needed according to empirical evidence in this domain, such as explainability, role-agnosticism, adaptability, and being utility- and value-driven.
We present ELVIRA, an agent that supports multiuser privacy, whose design meets all these requirements. By considering the sharing preferences and the moral values of users, ELVIRA identifies the optimal sharing policy. Furthermore, ELVIRA justifies the optimality of the solution through explanations based on argumentation. We prove via simulations that ELVIRA provides solutions with the best trade-off between individual utility and value adherence. We also show through a user study that ELVIRA suggests solutions that are more acceptable than existing approaches and that its explanations are also more satisfactory.

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