%% This BibTeX bibliography file was created using BibDesk.
%% https://bibdesk.sourceforge.io/

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@webpage{Apple_2018,
	author = {Apple},
	date-added = {2024-02-29 20:15:58 +0100},
	date-modified = {2024-02-29 20:16:33 +0100},
	keywords = {Voice Assistants},
	lastchecked = {2024-02-29},
	month = {04},
	title = {Personalized Hey Siri},
	url = {https://machinelearning.apple.com/research/personalized-hey-siri},
	year = {2018}}

@webpage{Hey_Siri_Commands,
	date-added = {2024-02-29 17:23:49 +0100},
	date-modified = {2024-02-29 17:24:43 +0100},
	keywords = {Voice Assistants},
	lastchecked = {2024-02-29},
	title = {Hey Siri: Commands in English},
	url = {https://www.trendvektor.de/files/trendvektor/stories/notice/Hey-Siri.pdf}}

@article{Apple_2023,
	author = {Apple},
	date-added = {2024-02-29 11:52:30 +0100},
	date-modified = {2024-02-29 11:53:27 +0100},
	keywords = {Voice Assistants},
	month = {08},
	title = {Voice Trigger System for Siri},
	urldate = {https://machinelearning.apple.com/research/voice-trigger},
	year = {2023}}

@article{Ateniese_2015aa,
	abstract = {News reports of the last few years indicated that several intelligence agencies are able to monitor large networks or entire portions of the Internet backbone. Such a powerful adversary has only recently been considered by the academic literature. In this paper, we propose a new adversary model for Location Based Services (LBSs). The model takes into account an unauthorized third party, different from the LBS provider itself, that wants to infer the location and monitor the movements of a LBS user. We show that such an adversary can extrapolate the position of a target user by just analyzing the size and the timing of the encrypted traffic exchanged between that user and the LBS provider. We performed a thorough analysis of a widely deployed location based app that comes pre-installed with many Android devices: GoogleNow. The results are encouraging and highlight the importance of devising more effective countermeasures against powerful adversaries to preserve the privacy of LBS users.},
	author = {Giuseppe Ateniese and Briland Hitaj and Luigi V. Mancini and Nino V. Verde and Antonio Villani},
	date-added = {2024-02-29 10:57:23 +0100},
	date-modified = {2024-02-29 10:57:39 +0100},
	eprint = {1505.07774},
	keywords = {Traffic Fingerprinting},
	month = {05},
	title = {No Place to Hide that Bytes won't Reveal: Sniffing Location-Based Encrypted Traffic to Track a User's Position},
	url = {https://arxiv.org/pdf/1505.07774.pdf},
	year = {2015},
	bdsk-url-1 = {https://arxiv.org/pdf/1505.07774.pdf},
	bdsk-url-2 = {https://arxiv.org/abs/1505.07774},
	bdsk-file-1 = {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}}

@article{Gupta_2023,
	author = {Gupta, Maanak and Akiri, Charankumar and Aryal, Kshitiz and Parker, Eli and Praharaj, Lopamudra},
	date-added = {2024-02-29 01:53:56 +0100},
	date-modified = {2024-02-29 01:54:31 +0100},
	doi = {10.1109/ACCESS.2023.3300381},
	journal = {IEEE Access},
	keywords = {Background},
	pages = {80218-80245},
	title = {From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy},
	volume = {11},
	year = {2023},
	bdsk-url-1 = {https://doi.org/10.1109/ACCESS.2023.3300381},
	bdsk-file-1 = {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}}

@article{Google_LLM_2024,
	author = {Google},
	date-added = {2024-02-29 01:45:24 +0100},
	date-modified = {2024-02-29 01:48:17 +0100},
	keywords = {Background},
	month = {01},
	title = {Gemini: A Family of Highly Capable Multimodal Models},
	url = {https://storage.googleapis.com/deepmind-media/gemini/gemini_1_report.pdf},
	year = {2024}}

@webpage{Amazon_LLM_2023,
	author = {Daniel Rausch},
	date-added = {2024-02-29 01:36:15 +0100},
	date-modified = {2024-02-29 01:37:40 +0100},
	keywords = {Background},
	lastchecked = {2024-02-29},
	month = {09},
	title = {Previewing the future of Alexa},
	url = {https://www.aboutamazon.com/news/devices/amazon-alexa-generative-ai},
	year = {2023}}

@inproceedings{Zhang_2018,
	author = {Zhang, Yiting and Yang, Ming and Gu, Xiaodan and Pan, Peilong and Ling, Zhen},
	booktitle = {2018 Sixth International Conference on Advanced Cloud and Big Data (CBD)},
	date-added = {2024-02-29 00:59:55 +0100},
	date-modified = {2024-02-29 01:00:57 +0100},
	doi = {10.1109/CBD.2018.00052},
	keywords = {Background},
	pages = {249-256},
	title = {Fingerprinting Network Device Based on Traffic Analysis in High-Speed Network Environment},
	year = {2018},
	bdsk-url-1 = {https://doi.org/10.1109/CBD.2018.00052},
	bdsk-file-1 = {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}}

@misc{rfc6716,
	abstract = {This document defines the Opus interactive speech and audio codec. Opus is designed to handle a wide range of interactive audio applications, including Voice over IP, videoconferencing, in-game chat, and even live, distributed music performances. It scales from low bitrate narrowband speech at 6 kbit/s to very high quality stereo music at 510 kbit/s. Opus uses both Linear Prediction (LP) and the Modified Discrete Cosine Transform (MDCT) to achieve good compression of both speech and music. {[}STANDARDS-TRACK{]}},
	author = {Jean-Marc Valin and Koen Vos and Timothy B. Terriberry},
	date-modified = {2024-02-26 18:34:20 +0100},
	doi = {10.17487/RFC6716},
	howpublished = {RFC 6716},
	keywords = {Background},
	month = sep,
	number = 6716,
	pagetotal = 326,
	publisher = {RFC Editor},
	series = {Request for Comments},
	title = {{Definition of the Opus Audio Codec}},
	url = {https://www.rfc-editor.org/info/rfc6716},
	year = 2012,
	bdsk-url-1 = {https://www.rfc-editor.org/info/rfc6716},
	bdsk-url-2 = {https://doi.org/10.17487/RFC6716}}

@misc{rfc1951,
	abstract = {This specification defines a lossless compressed data format that compresses data using a combination of the LZ77 algorithm and Huffman coding, with efficiency comparable to the best currently available general-purpose compression methods. This memo provides information for the Internet community. This memo does not specify an Internet standard of any kind.},
	author = {L. Peter Deutsch},
	date-modified = {2024-02-26 18:31:09 +0100},
	doi = {10.17487/RFC1951},
	howpublished = {RFC 1951},
	keywords = {Background},
	month = may,
	number = 1951,
	pagetotal = 17,
	publisher = {RFC Editor},
	series = {Request for Comments},
	title = {{DEFLATE Compressed Data Format Specification version 1.3}},
	url = {https://www.rfc-editor.org/info/rfc1951},
	year = 1996,
	bdsk-url-1 = {https://www.rfc-editor.org/info/rfc1951},
	bdsk-url-2 = {https://doi.org/10.17487/RFC1951}}

@article{Seagraves_2022,
	author = {Andrew Seagraves},
	date-added = {2024-02-24 12:30:56 +0100},
	date-modified = {2024-02-24 14:31:56 +0100},
	keywords = {Background},
	lastchecked = {2024-02-24},
	month = {12},
	title = {Benchmarking Top Open Source Speech Recognition Models: Whisper, Facebook wav2vec2, and Kaldi},
	url = {https://deepgram.com/learn/benchmarking-top-open-source-speech-models},
	year = {2022},
	bdsk-url-1 = {https://deepgram.com/learn/benchmarking-top-open-source-speech-models}}

@article{Radford_2022aa,
	abstract = {We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results but in a zero-shot transfer setting without the need for any fine-tuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and inference code to serve as a foundation for further work on robust speech processing.},
	author = {Alec Radford and Jong Wook Kim and Tao Xu and Greg Brockman and Christine McLeavey and Ilya Sutskever},
	date-added = {2024-02-24 12:22:44 +0100},
	date-modified = {2024-02-24 12:22:59 +0100},
	eprint = {2212.04356},
	keywords = {Background},
	month = {12},
	title = {Robust Speech Recognition via Large-Scale Weak Supervision},
	url = {https://arxiv.org/pdf/2212.04356.pdf},
	year = {2022},
	bdsk-file-1 = {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},
	bdsk-url-1 = {https://arxiv.org/pdf/2212.04356.pdf},
	bdsk-url-2 = {https://arxiv.org/abs/2212.04356}}

@webpage{Microsoft_Copilot_2023,
	date-added = {2024-02-22 18:06:06 +0100},
	date-modified = {2024-02-22 18:06:51 +0100},
	keywords = {Background},
	lastchecked = {2024-02-22},
	month = {09},
	read = {1},
	title = {Announcing Microsoft Copilot, your everyday AI companion},
	url = {https://blogs.microsoft.com/blog/2023/09/21/announcing-microsoft-copilot-your-everyday-ai-companion/},
	year = {2023},
	bdsk-url-1 = {https://blogs.microsoft.com/blog/2023/09/21/announcing-microsoft-copilot-your-everyday-ai-companion/}}

@webpage{Microsoft_Cortana_2023,
	date-added = {2024-02-22 17:55:56 +0100},
	date-modified = {2024-02-22 17:57:57 +0100},
	keywords = {Background},
	lastchecked = {2024-02-22},
	month = {08},
	read = {1},
	title = {End of support for Cortana},
	url = {https://support.microsoft.com/en-us/topic/end-of-support-for-cortana-d025b39f-ee5b-4836-a954-0ab646ee1efa},
	year = {2023},
	bdsk-url-1 = {https://support.microsoft.com/en-us/topic/end-of-support-for-cortana-d025b39f-ee5b-4836-a954-0ab646ee1efa}}

@inproceedings{Haas_2022,
	author = {Haas, Gabriel and Rietzler, Michael and Jones, Matt and Rukzio, Enrico},
	booktitle = {CHI Conference on Human Factors in Computing Systems},
	collection = {CHI {\^a}22},
	date-added = {2024-02-22 12:26:30 +0100},
	date-modified = {2024-02-22 12:26:44 +0100},
	doi = {10.1145/3491102.3517684},
	keywords = {Background},
	month = apr,
	publisher = {ACM},
	series = {CHI {\^a}22},
	title = {Keep it Short: A Comparison of Voice Assistants{\^a} Response Behavior},
	url = {http://dx.doi.org/10.1145/3491102.3517684},
	year = {2022},
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	bdsk-url-1 = {http://dx.doi.org/10.1145/3491102.3517684}}

@article{Mazhar_2020aa,
	abstract = {As the smart home IoT ecosystem flourishes, it is imperative to gain a better understanding of the unique challenges it poses in terms of management, security, and privacy. Prior studies are limited because they examine smart home IoT devices in testbed environments or at a small scale. To address this gap, we present a measurement study of smart home IoT devices in the wild by instrumenting home gateways and passively collecting real-world network traffic logs from more than 200 homes across a large metropolitan area in the United States. We characterize smart home IoT traffic in terms of its volume, temporal patterns, and external endpoints along with focusing on certain security and privacy concerns. We first show that traffic characteristics reflect the functionality of smart home IoT devices such as smart TVs generating high volume traffic to content streaming services following diurnal patterns associated with human activity. While the smart home IoT ecosystem seems fragmented, our analysis reveals that it is mostly centralized due to its reliance on a few popular cloud and DNS services. Our findings also highlight several interesting security and privacy concerns in smart home IoT ecosystem such as the need to improve policy-based access control for IoT traffic, lack of use of application layer encryption, and prevalence of third-party advertising and tracking services. Our findings have important implications for future research on improving management, security, and privacy of the smart home IoT ecosystem.},
	author = {M. Hammad Mazhar and Zubair Shafiq},
	date-added = {2024-02-21 17:51:54 +0100},
	date-modified = {2024-02-21 17:51:59 +0100},
	eprint = {2001.08288},
	keywords = {Traffic Fingerprinting},
	month = {01},
	title = {Characterizing Smart Home IoT Traffic in the Wild},
	url = {https://arxiv.org/pdf/2001.08288.pdf},
	year = {2020},
	bdsk-file-1 = {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},
	bdsk-url-1 = {https://arxiv.org/abs/2001.08288},
	bdsk-url-2 = {https://arxiv.org/pdf/2001.08288.pdf}}

@article{Trimananda_2019aa,
	abstract = {Smart home devices are vulnerable to passive inference attacks based on network traffic, even in the presence of encryption. In this paper, we present PINGPONG, a tool that can automatically extract packet-level signatures for device events (e.g., light bulb turning ON/OFF) from network traffic. We evaluated PINGPONG on popular smart home devices ranging from smart plugs and thermostats to cameras, voice-activated devices, and smart TVs. We were able to: (1) automatically extract previously unknown signatures that consist of simple sequences of packet lengths and directions; (2) use those signatures to detect the devices or specific events with an average recall of more than 97%; (3) show that the signatures are unique among hundreds of millions of packets of real world network traffic; (4) show that our methodology is also applicable to publicly available datasets; and (5) demonstrate its robustness in different settings: events triggered by local and remote smartphones, as well as by homeautomation systems.},
	author = {Rahmadi Trimananda and Janus Varmarken and Athina Markopoulou and Brian Demsky},
	date-added = {2024-02-21 17:28:09 +0100},
	date-modified = {2024-02-21 17:28:13 +0100},
	eprint = {1907.11797},
	keywords = {Device Fingerprinting},
	month = {07},
	title = {PingPong: Packet-Level Signatures for Smart Home Device Events},
	url = {https://arxiv.org/pdf/1907.11797.pdf},
	year = {2019},
	bdsk-file-1 = {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},
	bdsk-url-1 = {https://arxiv.org/abs/1907.11797},
	bdsk-url-2 = {https://arxiv.org/pdf/1907.11797.pdf}}

@article{NPR_2022,
	date-added = {2024-02-10 14:28:19 +0100},
	date-modified = {2024-02-10 15:39:00 +0100},
	journal = {National Public Media Insights},
	keywords = {Background},
	read = {1},
	title = {The Smart Audio Report},
	year = {2022},
	bdsk-file-1 = {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}}

@webpage{Statista_Apple_2023,
	date-added = {2024-02-10 14:05:11 +0100},
	date-modified = {2024-02-10 14:10:13 +0100},
	keywords = {Background},
	lastchecked = {10.2.2024},
	month = {November},
	read = {1},
	title = {Global Sales of Apple HomePod Devices},
	url = {https://www.statista.com/statistics/1421706/apple-homepod-unit-sales/},
	year = {2023},
	bdsk-url-1 = {https://www.statista.com/statistics/1421706/apple-homepod-unit-sales/}}

@webpage{Amazon_2023,
	date-added = {2024-02-10 13:45:41 +0100},
	date-modified = {2024-02-10 14:11:24 +0100},
	keywords = {Background},
	lastchecked = {10.2.2024},
	month = {May},
	read = {1},
	title = {Global Sales of Amazon Alexa Enabled Devices},
	url = {https://press.aboutamazon.com/2023/5/amazon-introduces-four-all-new-echo-devices-sales-of-alexa-enabled-devices-surpass-half-a-billion},
	year = {2023},
	bdsk-url-1 = {https://press.aboutamazon.com/2023/5/amazon-introduces-four-all-new-echo-devices-sales-of-alexa-enabled-devices-surpass-half-a-billion}}

@inproceedings{9102875,
	author = {Han, Yaowei and Li, Sheng and Cao, Yang and Ma, Qiang and Yoshikawa, Masatoshi},
	booktitle = {2020 IEEE International Conference on Multimedia and Expo (ICME)},
	date-added = {2023-03-06 16:56:23 +0100},
	date-modified = {2023-03-06 16:57:06 +0100},
	doi = {10.1109/ICME46284.2020.9102875},
	keywords = {Voice Assistants},
	month = {July},
	pages = {1-6},
	title = {Voice-Indistinguishability: Protecting Voiceprint In Privacy-Preserving Speech Data Release},
	year = {2020},
	bdsk-file-1 = {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},
	bdsk-url-1 = {https://doi.org/10.1109/ICME46284.2020.9102875}}

@article{schonherr2022101328,
	abstract = {Voice assistants like Amazon's Alexa, Google's Assistant, Tencent's Xiaowei, or Apple's Siri, have become the primary (voice) interface in smart speakers that can be found in millions of households. For privacy reasons, these speakers analyze every sound in their environment for their respective wake word like ``Alexa,'' ``Ji{\v u}s{\`\i}'{\`e}r l{\'\i}ng,'' or ``Hey Siri,'' before uploading the audio stream to the cloud for further processing. Previous work reported on examples of an inaccurate wake word detection, which can be tricked using similar words or sounds like ``cocaine noodles'' instead of ``OK Google.'' In this paper, we perform a comprehensive analysis of such accidental triggers, i.e., sounds that should not have triggered the voice assistant, but did. More specifically, we automate the process of finding accidental triggers and measure their prevalence across 11 smart speakers from 8 different manufacturers using everyday media such as TV shows, news, and other kinds of audio datasets. To systematically detect accidental triggers, we describe a method to artificially craft such triggers using a pronouncing dictionary and a weighted, phone-based Levenshtein distance. In total, we have found hundreds of accidental triggers. Moreover, we explore potential gender and language biases and analyze the reproducibility. Finally, we discuss the resulting privacy implications of accidental triggers and explore countermeasures to reduce and limit their impact on users' privacy. To foster additional research on these sounds that mislead machine learning models, we publish a dataset of more than 350 verified triggers as a research artifact.},
	author = {Lea Sch{\"o}nherr and Maximilian Golla and Thorsten Eisenhofer and Jan Wiele and Dorothea Kolossa and Thorsten Holz},
	date-added = {2023-03-06 16:53:44 +0100},
	date-modified = {2023-03-06 17:19:17 +0100},
	doi = {https://doi.org/10.1016/j.csl.2021.101328},
	issn = {0885-2308},
	journal = {Computer Speech and Language},
	keywords = {Voice Assistants},
	month = {December},
	pages = {101328},
	title = {Exploring accidental triggers of smart speakers},
	url = {https://www.sciencedirect.com/science/article/pii/S0885230821001212},
	volume = {73},
	year = {2021},
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	bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S0885230821001212},
	bdsk-url-2 = {https://doi.org/10.1016/j.csl.2021.101328}}

@article{caviglione_2013,
	abstract = {AbstractThe evolution of handheld devices and wireless communication ignited a new wave of speech-driven services using an ``intelligent'' back-end to perform complex tasks, for example, real-time dictation and data retrieval. Hence, the network is of crucial importance, and its usage must be properly analysed. This paper discusses some basic behaviours of Siri in terms of traffic patterns, possible models and a short privacy/security assessment. Copyright {\copyright} 2013 John Wiley \& Sons, Ltd.},
	author = {Caviglione, L.},
	date-added = {2023-03-06 16:45:45 +0100},
	date-modified = {2023-03-06 16:58:25 +0100},
	doi = {https://doi.org/10.1002/ett.2697},
	eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/ett.2697},
	journal = {Transactions on Emerging Telecommunications Technologies},
	keywords = {Voice Assistants},
	month = {July},
	number = {4},
	pages = {664-669},
	title = {A first look at traffic patterns of Siri},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ett.2697},
	volume = {26},
	year = {2013},
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	bdsk-url-1 = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ett.2697},
	bdsk-url-2 = {https://doi.org/10.1002/ett.2697}}

@article{Major_2021,
	author = {David Major and Danny Yuxing Huang and Marshini Chetty and Nick Feamster},
	date-added = {2023-03-06 10:58:55 +0100},
	date-modified = {2023-03-06 10:59:06 +0100},
	doi = {10.1145/3446389},
	journal = {{ACM} Transactions on Internet Technology},
	keywords = {Voice Assistants},
	month = {sep},
	number = {1},
	pages = {1--22},
	publisher = {Association for Computing Machinery ({ACM})},
	title = {Alexa, Who Am I Speaking To?: Understanding Users' Ability to Identify Third-Party Apps on Amazon Alexa},
	url = {https://doi.org/10.1145%2F3446389},
	volume = {22},
	year = 2021,
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	bdsk-url-1 = {https://doi.org/10.1145%2F3446389},
	bdsk-url-2 = {https://doi.org/10.1145/3446389}}

@inproceedings{Abdi_2021,
	author = {Noura Abdi and Xiao Zhan and Kopo M. Ramokapane and Jose Such},
	booktitle = {Proceedings of the 2021 {CHI} Conference on Human Factors in Computing Systems},
	date-added = {2023-03-06 10:57:37 +0100},
	date-modified = {2023-03-06 10:57:53 +0100},
	doi = {10.1145/3411764.3445122},
	keywords = {Voice Assistants},
	month = {may},
	publisher = {{ACM}},
	title = {Privacy Norms for Smart Home Personal Assistants},
	url = {https://doi.org/10.1145%2F3411764.3445122},
	year = 2021,
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	bdsk-url-1 = {https://doi.org/10.1145%2F3411764.3445122},
	bdsk-url-2 = {https://doi.org/10.1145/3411764.3445122}}

@article{9619970,
	author = {Edu, Jide S. and Ferrer-Aran, Xavier and Such, Jose and Suarez-Tangil, Guillermo},
	date-added = {2023-03-03 17:46:51 +0100},
	date-modified = {2023-03-03 17:48:34 +0100},
	doi = {10.1109/TDSC.2021.3129116},
	journal = {IEEE Transactions on Dependable and Secure Computing},
	keywords = {Voice Assistants},
	month = {November},
	number = {1},
	pages = {161-175},
	title = {SkillVet: Automated Traceability Analysis of Amazon Alexa Skills},
	volume = {20},
	year = {2021},
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	bdsk-url-1 = {https://doi.org/10.1109/TDSC.2021.3129116}}

@inproceedings{Edu_2022,
	author = {Jide Edu and Xavier Ferrer-Aran and Jose Such and Guillermo Suarez-Tangil},
	booktitle = {Proceedings of the {ACM} Web Conference 2022},
	date-added = {2023-03-03 13:18:01 +0100},
	date-modified = {2023-03-03 13:18:04 +0100},
	doi = {10.1145/3485447.3512289},
	keywords = {Voice Assistants},
	month = {apr},
	publisher = {{ACM}},
	title = {Measuring Alexa Skill Privacy Practices across Three Years},
	url = {https://doi.org/10.1145%2F3485447.3512289},
	year = 2022,
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	bdsk-url-1 = {https://doi.org/10.1145%2F3485447.3512289},
	bdsk-url-2 = {https://doi.org/10.1145/3485447.3512289}}

@article{Bolton_2021,
	author = {Tom Bolton and Tooska Dargahi and Sana Belguith and Mabrook S. Al-Rakhami and Ali Hassan Sodhro},
	date-added = {2023-03-03 12:48:28 +0100},
	date-modified = {2023-03-03 13:04:49 +0100},
	doi = {10.3390/s21072312},
	journal = {Sensors},
	keywords = {Voice Assistants},
	month = {mar},
	number = {7},
	pages = {2312},
	publisher = {{MDPI} {AG}},
	read = {1},
	title = {On the Security and Privacy Challenges of Virtual Assistants},
	url = {https://doi.org/10.3390%2Fs21072312},
	volume = {21},
	year = 2021,
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	bdsk-url-1 = {https://doi.org/10.3390%2Fs21072312},
	bdsk-url-2 = {https://doi.org/10.3390/s21072312}}

@incollection{Xie_2022,
	author = {Fuman Xie and Yanjun Zhang and Hanlin Wei and Guangdong Bai},
	booktitle = {Advanced Data Mining and Applications},
	date-added = {2023-03-03 12:36:41 +0100},
	date-modified = {2023-03-03 12:40:21 +0100},
	doi = {10.1007/978-3-030-95405-5_12},
	keywords = {Voice Assistants},
	month = {January},
	pages = {159--173},
	publisher = {Springer International Publishing},
	read = {1},
	title = {{UQ}-{AAS}21: A Comprehensive Dataset of Amazon Alexa Skills},
	url = {https://doi.org/10.1007%2F978-3-030-95405-5_12},
	year = 2022,
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	bdsk-url-1 = {https://doi.org/10.1007%2F978-3-030-95405-5_12},
	bdsk-url-2 = {https://doi.org/10.1007/978-3-030-95405-5_12}}

@inproceedings{8802686,
	author = {Kennedy, Sean and Li, Haipeng and Wang, Chenggang and Liu, Hao and Wang, Boyang and Sun, Wenhai},
	booktitle = {2019 IEEE Conference on Communications and Network Security (CNS)},
	date-added = {2023-03-03 12:16:53 +0100},
	date-modified = {2023-03-03 12:23:17 +0100},
	doi = {10.1109/CNS.2019.8802686},
	keywords = {Voice Assistants},
	month = {June},
	pages = {232-240},
	read = {1},
	title = {I Can Hear Your Alexa: Voice Command Fingerprinting on Smart Home Speakers},
	year = {2019},
	bdsk-file-1 = {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},
	bdsk-url-1 = {https://doi.org/10.1109/CNS.2019.8802686}}

@article{Wang_2020aa,
	abstract = {This paper investigates the privacy leakage of smart speakers under an encrypted traffic analysis attack, referred to as voice command fingerprinting. In this attack, an adversary can eavesdrop both outgoing and incoming encrypted voice traffic of a smart speaker, and infers which voice command a user says over encrypted traffic. We first built an automatic voice traffic collection tool and collected two large-scale datasets on two smart speakers, Amazon Echo and Google Home. Then, we implemented proof-of-concept attacks by leveraging deep learning. Our experimental results over the two datasets indicate disturbing privacy concerns. Specifically, compared to 1% accuracy with random guess, our attacks can correctly infer voice commands over encrypted traffic with 92.89\% accuracy on Amazon Echo. Despite variances that human voices may cause on outgoing traffic, our proof-of-concept attacks remain effective even only leveraging incoming traffic (i.e., the traffic from the server). This is because the AI-based voice services running on the server side response commands in the same voice and with a deterministic or predictable manner in text, which leaves distinguishable pattern over encrypted traffic. We also built a proof-of-concept defense to obfuscate encrypted traffic. Our results show that the defense can effectively mitigate attack accuracy on Amazon Echo to 32.18%.},
	author = {Chenggang Wang and Sean Kennedy and Haipeng Li and King Hudson and Gowtham Atluri and Xuetao Wei and Wenhai Sun and Boyang Wang},
	date-added = {2023-03-01 21:16:38 +0100},
	date-modified = {2023-03-03 12:11:20 +0100},
	eprint = {2005.09800},
	journal = {13th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec '20), July 8--10, 2020, Linz (Virtual Event), Austria},
	keywords = {Voice Assistants},
	month = {05},
	read = {1},
	title = {Fingerprinting Encrypted Voice Traffic on Smart Speakers with Deep Learning},
	url = {https://arxiv.org/pdf/2005.09800.pdf},
	year = {2020},
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	bdsk-url-1 = {https://arxiv.org/pdf/2005.09800.pdf}}

@incollection{Tsiatsikas_2023,
	author = {Zisis Tsiatsikas and Georgios Karopoulos and Georgios Kambourakis},
	booktitle = {Computer Security. {ESORICS} 2022 International Workshops},
	date-added = {2023-03-01 20:18:27 +0100},
	date-modified = {2023-03-06 16:59:15 +0100},
	doi = {10.1007/978-3-031-25460-4_10},
	keywords = {Traffic Fingerprinting},
	month = {February},
	pages = {177--190},
	publisher = {Springer International Publishing},
	title = {Measuring the Adoption of {TLS} Encrypted Client Hello Extension and Its Forebear in the Wild},
	url = {https://doi.org/10.1007%2F978-3-031-25460-4_10},
	year = 2023,
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	bdsk-url-1 = {https://doi.org/10.1007%2F978-3-031-25460-4_10},
	bdsk-url-2 = {https://doi.org/10.1007/978-3-031-25460-4_10}}

@inproceedings{255322,
	author = {Zhixiu Guo and Zijin Lin and Pan Li and Kai Chen},
	booktitle = {29th USENIX Security Symposium (USENIX Security 20)},
	date-added = {2023-02-21 16:40:13 +0100},
	date-modified = {2023-02-24 03:01:48 +0100},
	isbn = {978-1-939133-17-5},
	keywords = {Voice Assistants},
	month = aug,
	pages = {2649--2666},
	publisher = {USENIX Association},
	read = {1},
	title = {{SkillExplorer}: Understanding the Behavior of Skills in Large Scale},
	url = {https://www.usenix.org/conference/usenixsecurity20/presentation/guo},
	year = {2020},
	bdsk-file-1 = {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},
	bdsk-url-1 = {https://www.usenix.org/conference/usenixsecurity20/presentation/guo}}

@inproceedings{Shezan_2020,
	author = {Faysal Hossain Shezan and Hang Hu and Jiamin Wang and Gang Wang and Yuan Tian},
	booktitle = {Proceedings of The Web Conference 2020},
	date-added = {2023-02-21 16:37:13 +0100},
	date-modified = {2023-02-23 17:27:07 +0100},
	doi = {10.1145/3366423.3380179},
	keywords = {Voice Assistants},
	month = {apr},
	publisher = {{ACM}},
	read = {1},
	title = {Read Between the Lines: An Empirical Measurement of Sensitive Applications of Voice Personal Assistant Systems},
	url = {https://doi.org/10.1145%2F3366423.3380179},
	year = 2020,
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	bdsk-url-1 = {https://doi.org/10.1145%2F3366423.3380179},
	bdsk-url-2 = {https://doi.org/10.1145/3366423.3380179}}

@article{Ford2019,
	abstract = {With approximately 8.2 million Echo family devices sold since 2014, Amazon controls 70{\%} of the intelligent personal assistant market. Amazon's Alexa Voice Service (AVS) provides voice control services for Amazon's Echo product line and various home automation devices such as thermostats and security cameras. In November 2017, Amazon expanded Alexa services into the business intelligent assistant market with Alexa for Business. As corporations integrate Alexa into their corporate networks, it is important that information technology security stakeholders understand Alexa's audio streaming network behavior in order to properly implement security countermeasures and policies. This paper contributes to the intelligent personal assistant knowledge domain by providing insight into Amazon Voice Services behavior by analyzing the network traffic of two Echo Dots over a 21-day period. The Echo Dots were installed in a private residence, and at no time during the experiment did family members or house guests purposely interact with the Echos. All recorded audio commands were inadvertent. Using a k-mean cluster analysis, this study established a quantifiable AVS network signature. Then, by comparing that AVS signature and logged Alexa audio commands to the 21-day network traffic dataset, this study confirmed disabling the Echo's microphone, with the on/off button, prohibits audio recording and streaming to Alexa Voice Service. With 30--38{\%} of Echo Dots' spurious audio recordings were human conversations, these findings support the Echo Dot recorded private home conversations and not all audio recordings are properly logged the Alexa Application. While further Alexa network traffic studies are needed, this study offers a network signature capable of identifying AVS network traffic.},
	author = {Ford, Marcia and Palmer, William},
	date-added = {2023-02-21 16:35:23 +0100},
	date-modified = {2023-02-23 22:40:01 +0100},
	day = {01},
	doi = {10.1007/s00779-018-1174-x},
	issn = {1617-4917},
	journal = {Personal and Ubiquitous Computing},
	keywords = {Voice Assistants},
	month = {Feb},
	number = {1},
	pages = {67--79},
	read = {1},
	title = {Alexa, are you listening to me? An analysis of Alexa voice service network traffic},
	url = {https://link.springer.com/content/pdf/10.1007/s00779-018-1174-x.pdf},
	volume = {23},
	year = {2019},
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	bdsk-url-1 = {https://link.springer.com/content/pdf/10.1007/s00779-018-1174-x.pdf},
	bdsk-url-2 = {https://doi.org/10.1007/s00779-018-1174-x}}

@inproceedings{Natatsuka_2019,
	author = {Atsuko Natatsuka and Ryo Iijima and Takuya Watanabe and Mitsuaki Akiyama and Tetsuya Sakai and Tatsuya Mori},
	booktitle = {Proceedings of the 2019 {ACM} {SIGSAC} Conference on Computer and Communications Security},
	date-added = {2023-02-21 16:29:35 +0100},
	date-modified = {2023-02-22 11:20:14 +0100},
	doi = {10.1145/3319535.3363274},
	keywords = {Voice Assistants},
	month = {nov},
	publisher = {{ACM}},
	read = {1},
	title = {Poster: A First Look at the Privacy Risks of Voice Assistant App},
	url = {https://doi.org/10.1145%2F3319535.3363274},
	year = 2019,
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	bdsk-url-1 = {https://doi.org/10.1145%2F3319535.3363274},
	bdsk-url-2 = {https://doi.org/10.1145/3319535.3363274}}

@article{Dubois_2020,
	author = {Daniel J. Dubois and Roman Kolcun and Anna Maria Mandalari and Muhammad Talha Paracha and David Choffnes and Hamed Haddadi},
	date-added = {2023-02-21 16:27:23 +0100},
	date-modified = {2023-03-01 17:14:49 +0100},
	doi = {10.2478/popets-2020-0072},
	journal = {Proceedings on Privacy Enhancing Technologies},
	keywords = {Voice Assistants},
	month = {aug},
	number = {4},
	pages = {255--276},
	publisher = {Privacy Enhancing Technologies Symposium Advisory Board},
	read = {1},
	title = {When Speakers Are All Ears: Characterizing Misactivations of {IoT} Smart Speakers},
	url = {https://doi.org/10.2478%2Fpopets-2020-0072},
	volume = {2020},
	year = 2020,
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	bdsk-url-1 = {https://doi.org/10.2478%2Fpopets-2020-0072},
	bdsk-url-2 = {https://doi.org/10.2478/popets-2020-0072}}

@inproceedings{Laperdrix_2016,
	author = {Pierre Laperdrix and Walter Rudametkin and Benoit Baudry},
	booktitle = {2016 {IEEE} Symposium on Security and Privacy ({SP})},
	date-added = {2023-02-08 11:08:21 +0100},
	date-modified = {2023-02-08 11:17:07 +0100},
	doi = {10.1109/sp.2016.57},
	keywords = {Device Fingerprinting},
	month = {may},
	publisher = {{IEEE}},
	title = {Beauty and the Beast: Diverting Modern Web Browsers to Build Unique Browser Fingerprints},
	url = {https://doi.org/10.1109%2Fsp.2016.57},
	year = 2016,
	bdsk-file-1 = {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},
	bdsk-url-1 = {https://doi.org/10.1109%2Fsp.2016.57},
	bdsk-url-2 = {https://doi.org/10.1109/sp.2016.57}}

@inproceedings{Wang_2020,
	author = {Tao Wang},
	booktitle = {2020 {IEEE} Symposium on Security and Privacy ({SP})},
	date-added = {2023-02-08 11:07:30 +0100},
	date-modified = {2023-02-22 09:44:58 +0100},
	doi = {10.1109/sp40000.2020.00015},
	keywords = {Traffic Fingerprinting},
	month = {may},
	publisher = {{IEEE}},
	read = {1},
	title = {High Precision Open-World Website Fingerprinting},
	url = {https://doi.org/10.1109%2Fsp40000.2020.00015},
	year = 2020,
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	bdsk-url-1 = {https://doi.org/10.1109%2Fsp40000.2020.00015},
	bdsk-url-2 = {https://doi.org/10.1109/sp40000.2020.00015}}

@incollection{Eckersley_2010,
	author = {Peter Eckersley},
	booktitle = {Privacy Enhancing Technologies},
	date-added = {2023-02-08 11:02:55 +0100},
	date-modified = {2023-02-08 11:17:07 +0100},
	doi = {10.1007/978-3-642-14527-8_1},
	keywords = {Device Fingerprinting},
	pages = {1--18},
	publisher = {Springer Berlin Heidelberg},
	title = {How Unique Is Your Web Browser?},
	url = {https://doi.org/10.1007%2F978-3-642-14527-8_1},
	year = 2010,
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	bdsk-url-2 = {https://doi.org/10.1007/978-3-642-14527-8_1}}

@article{Oh_2017,
	author = {Oh, Se and Li, Shuai and Hopper, Nicholas},
	date-added = {2023-02-08 11:01:10 +0100},
	date-modified = {2024-02-29 10:59:04 +0100},
	doi = {10.1515/popets-2017-0048},
	journal = {Proceedings on Privacy Enhancing Technologies},
	keywords = {Traffic Fingerprinting},
	month = {10},
	title = {Fingerprinting Keywords in Search Queries over Tor},
	volume = {2017},
	year = {2017},
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	bdsk-url-1 = {https://doi.org/10.1515/popets-2017-0048}}

@inproceedings{Zuo_2019,
	author = {Chaoshun Zuo and Haohuang Wen and Zhiqiang Lin and Yinqian Zhang},
	booktitle = {Proceedings of the 2019 {ACM} {SIGSAC} Conference on Computer and Communications Security},
	date-added = {2023-02-08 10:38:04 +0100},
	date-modified = {2023-02-08 11:16:42 +0100},
	doi = {10.1145/3319535.3354240},
	keywords = {Device Fingerprinting},
	month = {nov},
	publisher = {{ACM}},
	title = {Automatic Fingerprinting of Vulnerable {BLE} {IoT} Devices with Static {UUIDs} from Mobile Apps},
	url = {https://doi.org/10.1145%2F3319535.3354240},
	year = 2019,
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	bdsk-url-1 = {https://doi.org/10.1145%2F3319535.3354240},
	bdsk-url-2 = {https://doi.org/10.1145/3319535.3354240}}

@inproceedings{Das_2014,
	author = {Anupam Das and Nikita Borisov and Matthew Caesar},
	booktitle = {Proceedings of the 2014 {ACM} {SIGSAC} Conference on Computer and Communications Security},
	date-added = {2023-02-08 10:37:17 +0100},
	date-modified = {2023-02-08 11:17:07 +0100},
	doi = {10.1145/2660267.2660325},
	keywords = {Device Fingerprinting},
	month = {nov},
	publisher = {{ACM}},
	title = {Do You Hear What I Hear?},
	url = {https://doi.org/10.1145%2F2660267.2660325},
	year = 2014,
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	bdsk-url-1 = {https://doi.org/10.1145%2F2660267.2660325},
	bdsk-url-2 = {https://doi.org/10.1145/2660267.2660325}}

@inproceedings{Brik_2008,
	author = {Vladimir Brik and Suman Banerjee and Marco Gruteser and Sangho Oh},
	booktitle = {Proceedings of the 14th {ACM} international conference on Mobile computing and networking},
	date-added = {2023-02-08 10:36:39 +0100},
	date-modified = {2023-02-08 11:17:07 +0100},
	doi = {10.1145/1409944.1409959},
	keywords = {Device Fingerprinting},
	month = {sep},
	publisher = {{ACM}},
	title = {Wireless device identification with radiometric signatures},
	url = {https://doi.org/10.1145%2F1409944.1409959},
	year = 2008,
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	bdsk-url-1 = {https://doi.org/10.1145%2F1409944.1409959},
	bdsk-url-2 = {https://doi.org/10.1145/1409944.1409959}}

@article{Papadogiannaki_2021,
	author = {Eva Papadogiannaki and Sotiris Ioannidis},
	date-added = {2023-02-08 10:35:40 +0100},
	date-modified = {2023-02-08 11:16:02 +0100},
	doi = {10.1145/3457904},
	journal = {{ACM} Computing Surveys},
	keywords = {Traffic Fingerprinting},
	month = {jul},
	number = {6},
	pages = {1--35},
	publisher = {Association for Computing Machinery ({ACM})},
	title = {A Survey on Encrypted Network Traffic Analysis Applications, Techniques, and Countermeasures},
	url = {https://doi.org/10.1145%2F3457904},
	volume = {54},
	year = 2021,
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	bdsk-url-1 = {https://doi.org/10.1145%2F3457904},
	bdsk-url-2 = {https://doi.org/10.1145/3457904}}

@inproceedings{Gonzalez_2016,
	author = {Roberto Gonzalez and Claudio Soriente and Nikolaos Laoutaris},
	booktitle = {Proceedings of the 2016 Internet Measurement Conference},
	date-added = {2023-02-08 10:34:36 +0100},
	date-modified = {2023-03-01 21:03:50 +0100},
	doi = {10.1145/2987443.2987451},
	keywords = {Traffic Fingerprinting},
	month = {nov},
	publisher = {{ACM}},
	read = {1},
	title = {User Profiling in the Time of {HTTPS}},
	url = {https://doi.org/10.1145%2F2987443.2987451},
	year = 2016,
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