Speech Privacy Protection based on Controlling Estimated Speech Transmission Index

Phunruangsakao, Chatrin; Kraikhun, Phrimphissa; Duangpummet, Suradej; Karnjana, Jessada; Unoki, Masashi; Kongprawechnon, Waree

doi:10.1109/ecti-con49241.2020.9158131

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…The research community has attempted to develop some defenses to relevant attacks in both audio domains [7,33,34,47,51,52] and vibration domains [57,62]. However, it seems certain physical parameters can be exploited to easily thwart such attacks.…”

Section: Discussionmentioning

confidence: 99%

SoK

Walker

Saxena

2021

Proceedings of the 14th ACM Conference on Security and Privacy in Wireless and Mobile Networks

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Existing academic research on vibration-based speech attacks has introduced interesting and intellectually appealing threat vectors with proof-of-concept demonstrations in controlled environments. The attacks presented in these studies exploit different types of sensors such as MEMS motion sensors, laser-based sensors, and some other sensors (camera, position error signal, piezo-disc) to measure the vibrations induced on an object by nearby sensitive speech. Such sensors are commonly found on mobile devices like smartphones and tablets that can be exposed to sensitive speech, revealing the significance of this potential threat. These studies have amassed significant attention in news and media and introduced concern to people about the safety of their day-to-day speech and around their personal, wireless and IoT devices. However, we hypothesize that the controlled experiments in the prior research maintain critical parameter values that are favorable to attack success (deviating from the limiting settings in a real-world scenario) and produce results that suggest a greater real-life threat level than actually exists.The contributions made in this paper are as follows; First, we provide a detailed review of 10 existing academic research works related to vibration-based eavesdropping attacks. Second, we identify key experimental parameters that can impact the success of eavesdropping in the vibration domain. Third, we build a framework to evaluate the existing literature based on the Percent Parameters in Favored Settings (PPFS) Score metric that we define. Lastly, we use our defined framework to evaluate the feasibility of the existing vibration-based speech attacks to compromise live human speech to the extent of full speech recognition. The results of our evaluation suggest that none of the existing vibration-based eavesdropping attacks have a high likelihood of successfully compromising live human speech in a real-world scenario.

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Section: Discussionmentioning

confidence: 99%

SoK

Walker

Saxena

2021

Proceedings of the 14th ACM Conference on Security and Privacy in Wireless and Mobile Networks

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“…However, this approach provides a blanket solution. It is heavily dependent on specific room and speaker characteristics, making it challenging for the technology to generalize to a variety of scenarios [4].…”

Section: Introductionmentioning

confidence: 99%

Revisiting Speech Content Privacy

Williams¹,

Yamagishi²,

Paul-Gauthier³

et al. 2021

2021 ISCA Symposium on Security and Privacy in Speech Communication

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In this paper, we discuss an important aspect of speech privacy: protecting spoken content. New capabilities from the field of machine learning provide a unique and timely opportunity to revisit speech content protection. There are many different applications of content privacy, even though this area has been underexplored in speech technology research. This paper presents several scenarios that indicate a need for speech content privacy even as the specific techniques to achieve content privacy may necessarily vary. Our discussion includes several different types of content privacy including recoverable and non-recoverable content. Finally, we introduce evaluation strategies as well as describe some of the difficulties that may be encountered.

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Speech Privacy Protection based on Controlling Estimated Speech Transmission Index

Cited by 2 publications

References 4 publications

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Revisiting Speech Content Privacy

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