Tracking, Profiling, and Ad Targeting in the Alexa Echo Smart Speaker Ecosystem
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Abstract (arXiv)
Smart speakers collect voice commands, which can be used to infer sensitive information about users. Given the potential for privacy harms, there is a need for greater transparency and control over the data collected, used, and shared by smart speaker platforms as well as third party skills supported on them. To bridge this gap, we build a framework to measure data collection, usage, and sharing by the smart speaker platforms. We apply our framework to the Amazon smart speaker ecosystem. Our results show that Amazon and third parties, including advertising and tracking services that are unique to the smart speaker ecosystem, collect smart speaker interaction data. We also find that Amazon processes smart speaker interaction data to infer user interests and uses those inferences to serve targeted ads to users. Smart speaker interaction also leads to ad targeting and as much as 30X higher bids in ad auctions, from third party advertisers. Finally, we find that Amazon's and third party skills' data practices are often not clearly disclosed in their policy documents.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Apr 2022.
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Citing papers per year: 2023: 2; 2024: 1; 2025: 2; 2026: 2.
7 papers.
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- 2026Maintaining IoT Device Identification under Concept Drift via Budget-Aware Traffic Labeling16 Aug 2026
- 2026PromptPET: Privacy-Utility Optimized Prompt Obfuscation3 Jul 2026
- 2025Towards Automating Data Access Permissions in AI Agents22 Nov 2025
- 2025Safeguarding Privacy in Edge Speech Understanding with Tiny Foundation Models29 Jan 2025
- 2024Analyzing Consumer IoT Traffic from Security and Privacy Perspectives: a Comprehensive Survey24 Mar 2024
- 2023LLM Platform Security: Applying a Systematic Evaluation Framework to OpenAI's ChatGPT Plugins19 Sep 2023
- 2023On the Impact of Voice Anonymization on Speech Diagnostic Applications: a Case Study on COVID-19 Detection5 Apr 2023