Leakage of Sensitive Information to Third-Party Voice Applications

Bispham, Mary; Zard, Clara; Ferrer-Aran, Xavier; Suárez-Tangil, Guillermo; Such, José M.

doi:10.1145/3543829.3544520

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…Many research prototypes and currently available HVAs are built with or on top of general-purpose commercial voice assistants [167]. The security and privacy of voice assistants in general is an active area of research-see [54,165] for a systematic review on the topic-and several issues have been revealed, including the extensive collection of sensitive user data, unauthorized access and questionable practices by third-parties and skills [25,52,53,70,161], along with inadequacies in the development and application of privacy policies [53,113]. Researchers are actively working on a range of solutions, such as mechanisms for providing user with meaningful verbal consent [31,162] and automated systems to identify policy breaches [52,207].…”

Section: Implications Unique To Healthcare Voice Assistantsmentioning

confidence: 99%

Healthcare Voice AI Assistants: Factors Influencing Trust and Intention to Use

Zhan,

Abdi,

Seymour

et al. 2024

Proc. ACM Hum.-Comput. Interact.

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AI assistants such as Alexa, Google Assistant, and Siri, are making their way into the healthcare sector, offering a convenient way for users to access different healthcare services. Trust is a vital factor in the uptake of healthcare services, but the factors affecting trust in voice assistants used for healthcare are under-explored and this specialist domain introduces additional requirements. This study explores the effects of different functional, personal, and risk factors on trust in and adoption of healthcare voice AI assistants (HVAs), generating a partial least squares structural model from a survey of 300 voice assistant users. Our results indicate that trust in HVAs can be significantly explained by functional factors (usefulness, content credibility, quality of service relative to a healthcare professional), together with security, and privacy risks and personal stance in technology. We also discuss differences in terms of trust between HVAs and general-purpose voice assistants as well as implications that are unique to HVAs.

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Section: Implications Unique To Healthcare Voice Assistantsmentioning

confidence: 99%

Healthcare Voice AI Assistants: Factors Influencing Trust and Intention to Use

Zhan,

Abdi,

Seymour

et al. 2024

Proc. ACM Hum.-Comput. Interact.

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“…Other avenues of exploration could include the verification of fact sources and mandatory communication to users about the source of a skill's information before and during use. Exploration of this topic represents an exciting opportunity for future work, but will be made difficult by the dominant architecture where skills are hosted by third party developers outside of the control of vendors (resulting in difficulties when vetting and verifying third party software) with very recent work showing evidence of third-party skills serving misinformation [21].…”

Section: Unexpected Discoveriesmentioning

confidence: 99%

A Systematic Review of Ethical Concerns with Voice Assistants

Seymour¹,

Zhan²,

Cote³

et al. 2023

Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society

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“…Additionally, to provide their vast range of services, they use both built-in skills and third-party applications called skills [14,5]. The disadvantage of this complex ecosystem is that users' personal information may be accessed or misused by unauthorised parties without the user's awareness [44,15,10]. For instance, this may occur when Spotify's service provider accesses users' login details while playing music via Alexa [6], and everyone within audible range may know the status of a smart door lock [34].…”

Section: Introductionmentioning

confidence: 99%

Privacy-Enhanced AI Assistants Based on Dialogues and Case Similarity

Zhan,

Sarkadi,

Such

2023

Frontiers in Artificial Intelligence and Applications

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Personal assistants (PAs) such as Amazon Alexa, Google Assistant and Apple Siri are now widespread. However, without adequate safeguards and controls their use may lead to privacy risks and violations. In this paper, we propose a model for privacy-enhancing PAs. The model is an interpretable AI architecture that combines 1) a dialogue mechanism for understanding the user and getting online feedback from them, with 2) a decision-making mechanism based on case-based reasoning considering both user and scenario similarity. We evaluate our model using real data about users’ privacy preferences, and compare its accuracy and demand for user involvement with both online machine learning and other, more interpretable, AI approaches. Our results show that our proposed architecture is more accurate and requires less intervention from the users than existing approaches.

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Leakage of Sensitive Information to Third-Party Voice Applications

Cited by 4 publications

References 9 publications

Healthcare Voice AI Assistants: Factors Influencing Trust and Intention to Use

Healthcare Voice AI Assistants: Factors Influencing Trust and Intention to Use

A Systematic Review of Ethical Concerns with Voice Assistants

Privacy-Enhanced AI Assistants Based on Dialogues and Case Similarity

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