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SkillBot: Identifying Risky Content for Children in Alexa Skills

Published:25 July 2022Publication History
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Abstract

Many households include children who use voice personal assistants (VPA) such as Amazon Alexa. Children benefit from the rich functionalities of VPAs and third-party apps but are also exposed to new risks in the VPA ecosystem. In this article, we first investigate “risky” child-directed voice apps that contain inappropriate content or ask for personal information through voice interactions. We build SkillBot—a natural language processing-based system to automatically interact with VPA apps and analyze the resulting conversations. We find 28 risky child-directed apps and maintain a growing dataset of 31,966 non-overlapping app behaviors collected from 3,434 Alexa apps. Our findings suggest that although child-directed VPA apps are subject to stricter policy requirements and more intensive vetting, children remain vulnerable to inappropriate content and privacy violations. We then conduct a user study showing that parents are concerned about the identified risky apps. Many parents do not believe that these apps are available and designed for families/kids, although these apps are actually published in Amazon’s “Kids” product category. We also find that parents often neglect basic precautions, such as enabling parental controls on Alexa devices. Finally, we identify a novel risk in the VPA ecosystem: confounding utterances or voice commands shared by multiple apps that may cause a user to interact with a different app than intended. We identify 4,487 confounding utterances, including 581 shared by child-directed and non-child-directed apps. We find that 27% of these confounding utterances prioritize invoking a non-child-directed app over a child-directed app. This indicates that children are at real risk of accidentally invoking non-child-directed apps due to confounding utterances.

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            cover image ACM Transactions on Internet Technology
            ACM Transactions on Internet Technology  Volume 22, Issue 3
            August 2022
            631 pages
            ISSN:1533-5399
            EISSN:1557-6051
            DOI:10.1145/3498359
            • Editor:
            • Ling Liu
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            Publication History

            • Published: 25 July 2022
            • Online AM: 30 May 2022
            • Accepted: 1 May 2022
            • Revised: 1 March 2022
            • Received: 1 September 2021
            Published in toit Volume 22, Issue 3

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