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PrivExtractor: Toward Redressing the Imbalance of Understanding between Virtual Assistant Users and Vendors

    Research output: Contribution to journalArticle (Academic Journal)peer-review

    2 Citations (Scopus)
    135 Downloads (Pure)

    Abstract

    The use of voice-controlled virtual assistants (VAs) is significant, and user numbers increase every year. Extensive use of VAs has provided the large, cash-rich technology companies who sell them with another way of consuming users data, providing a lucrative revenue stream. Whilst these companies are legally obliged to treat users information fairly and responsibly, artificial intelligence techniques used to process data have become incredibly sophisticated, leading to users concerns that a lack of clarity is making it hard to understand the nature and scope of data collection and use.

    There has been little work undertaken on a self-contained user awareness tool targeting VAs. PrivExtractor, a novel web-based awareness dashboard for VA users, intends to redress this imbalance of understanding between the data processors and the user. It aims to achieve this using the four largest VA vendors as a case study and providing a comparison function that examines the four companies privacy practices and their compliance with data protection law.

    As a result of this research, we conclude that the companies studied are largely compliant with the law, as expected. However, the user remains disadvantaged due to the ineffectiveness of current data regulation that does not oblige the companies to fully and transparently disclose how and when they use, share, or profit from the data. Furthermore, the software tool developed during the research is, we believe, the first that is capable of a
    Original languageEnglish
    Article number31
    Journal ACM Transactions on Privacy and Security
    Volume26
    Issue number3
    Early online date23 Mar 2023
    DOIs
    Publication statusPublished - 1 Aug 2023

    Bibliographical note

    Publisher Copyright:
    © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

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