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Measuring the Effectiveness of Privacy Policies for Voice Assistant Applications

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arxiv 2007.14570 v1 pith:TM5NETWU submitted 2020-07-29 cs.CR cs.CY

classification cs.CRcs.CY
keywords privacypoliciesamazonassistantdevelopersgoogleusersalexa
verification ladder T0 review T1 audit T2 compute T3 formal

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Voice Assistants (VA) such as Amazon Alexa and Google Assistant are quickly and seamlessly integrating into people's daily lives. The increased reliance on VA services raises privacy concerns such as the leakage of private conversations and sensitive information. Privacy policies play an important role in addressing users' privacy concerns and informing them about the data collection, storage, and sharing practices. VA platforms (both Amazon Alexa and Google Assistant) allow third-party developers to build new voice-apps and publish them to the app store. Voice-app developers are required to provide privacy policies to disclose their apps' data practices. However, little is known whether these privacy policies are informative and trustworthy or not on emerging VA platforms. On the other hand, many users invoke voice-apps through voice and thus there exists a usability challenge for users to access these privacy policies. In this paper, we conduct the first large-scale data analytics to systematically measure the effectiveness of privacy policies provided by voice-app developers on two mainstream VA platforms. We seek to understand the quality and usability issues of privacy policies provided by developers in the current app stores. We analyzed 64,720 Amazon Alexa skills and 2,201 Google Assistant actions. Our work also includes a user study to understand users' perspectives on VA's privacy policies. Our findings reveal a worrisome reality of privacy policies in two mainstream voice-app stores, where there exists a substantial number of problematic privacy policies. Surprisingly, Google and Amazon even have official voice-apps violating their own requirements regarding the privacy policy.

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Cited by 1 Pith paper

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  1. "I Apologize For Not Understanding Your Policy": Exploring the Specification and Evaluation of User-Managed Access Control Policies by AI Virtual Assistants

    cs.AI 2025-05 conditional novelty 5.0 of 10

    Current virtual assistants handle explicitly stated access control rules fairly well but often fail on inference-based and default-deny policy decisions, a pattern consistent across the three tested domains.

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