Best paper award at IMC ’23
Tracking, Profiling, and Ad Targeting in the Alexa Echo Smart Speaker Ecosystem
Umar Iqbal (University of California, Davis), Pouneh Nikkhah Bahrami (University of California, Davis), Rahmadi Trimananda (University of California, Irvine), Hao Cui (University of California, Irvine), Alexander Gamero-Garrido (Northeastern University), Daniel J. Dubois (Northeastern University), David Choffnes (Northeastern University), Athina Markopoulou (University of California, Irvine), Franziska Roesner (University of Washington), Zubair Shafiq (University of California, Davis)
Last updated: 11/21/2023
This is a summary page for this paper. The official page with all the material is available here.
NEWS
- 10/24/2023. This paper won the Best paper award at IMC 2023
ABSTRACT
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.
ABOUT THIS PUBLICATION
Paper title: Tracking, Profiling, and Ad Targeting in the Alexa Echo Smart Speaker Ecosystem.
Authors: Umar Iqbal (University of California, Davis), Pouneh Nikkhah Bahrami (University of California, Davis), Rahmadi Trimananda (University of California, Irvine), Hao Cui (University of California, Irvine), Alexander Gamero-Garrido (Northeastern University), Daniel J. Dubois (Northeastern University), David Choffnes (Northeastern University), Athina Markopoulou (University of California, Irvine), Franziska Roesner (University of Washington), Zubair Shafiq (University of California, Davis)
Paper official page (with all the material): available here
Full Text (PDF): available as ACM open access.
Citation:
@inproceedings{Iqbal23IMCAlexaEchos,
title = {Tracking, Profiling, and Ad Targeting in the Alexa Echo Smart Speaker Ecosystem},
author = {Umar Iqbal, Pouneh Nikkhah Bahrami, Rahmadi Trimananda, Hao Cui,
Alexander Gamero-Garrido, Daniel J. Dubois, David Choffnes, Athina Markopoulou,
Franziska Roesner, and Zubair Shafiq},
booktitle = {ACM Internet Measurement Conference (IMC)},
year = {2023}
}
ACKNOWLEDGMENTS
We would like to thank Caelan MacArthur who contributed to the preliminary investigation of this work in summer 2021 as part of the CRA-WP Distributed Research Experiences for Undergraduates (DREU) at the University of California, Davis. We would like to thank Jan Sanislo from the University of Washington IT team for their help with setting up a WiFi network for connecting Alexa Echo devices. This work is supported in part by the National Science Foundation under awards CNS-1956393, CNS-1955227, CNS2103439, CNS-2103038, CNS-2138139, CNS-2114230, CNS-1909020, Computing Research Association for the CIFellows 2021 Project under Grant CNS-2127309, the Northeastern University Future Faculty Fellowship (2021), and the Consumer Reports Innovation Lab.