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Abstract Audio-based sensing enables fine-grained human activity detection, such as sensing hand gestures and contact-free estimation of the breathing rate. A passive adversary, equipped with microphones, can leverage the ongoing sensing to infer private information about individuals. Further, with multiple microphones, a beamforming-capable adversary can defeat the previously-proposed privacy protection obfuscation techniques. Such an adversary can isolate the obfuscation signal and cancel it, even when situated behind a wall. AudioSentry is the first to address the privacy problem in audio sensing by protecting the users against a multi-microphone adversary. It utilizes the commodity and audio-capable devices, already available in the user’s environment, to form a distributed obfuscator array. AudioSentry packs a novel technique to carefully generate obfuscation beams in different directions, preventing the multi-microphone adversary from canceling the obfuscation signal. AudioSentry follows by a dynamic channel estimation scheme to preserve authorized sensing under obfuscation. AudioSentry offers the advantages of being practical to deploy and effective against an adversary with a large number of microphones. Our extensive evaluations with commodity devices show that protects the user’s privacy against a 16-microphone adversary with only four commodity obfuscators, regardless of the adversary’s position. AudioSentry provides its privacy-preserving features with little overhead on the authorized sensor.
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Null/Optimum Point Optimization for Indoor Passive Radar Motion Sensing
Indoor passive radar has gained traction as a method for measuring small-amplitude motions without requiring a cooperative signal to be transmitted by the sensor. Ubiquitous signals such as Wi-Fi and Bluetooth may be used as illuminators of opportunity in order to measure the motion of various targets. Both the direct, unmodulated signal as well as the Doppler-shifted signal are received at the radar and are used for down-conversion to baseband. Since there is no cooperative local oscillator used in passive radar, it is not currently possible to effectively extract both the I and Q channel data making null-point detection a returning problem. In this work, the null-point detection problem is analyzed theoretically to develop a simulation model for passive radar sensing. Using this model, an in-depth analysis is undertaken in order to determine the effectiveness of methods such as channel selection, frequency tuning, or multi-band/multi-static sensing in removing or mitigating the null-point detection problem. The results demonstrate that despite the presence of the null-point issue, it is possible to reduce its impact on motion detection and optimize the detection sensitivity.
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- PAR ID:
- 10457358
- Date Published:
- Journal Name:
- 2023 IEEE Radar Conference (RadarConf23)
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/RadarConf2351548.2023.10149758
