TY - GEN
T1 - Configurable Pulmonary-Tuned Privacy Preservation Algorithm for Mobile Devices
AU - Lee, Sujee
AU - Nemati, Ebrahim
AU - Kuang, Jilong
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/21
Y1 - 2019/1/21
N2 - Audio-based automated pulmonary symptom detection has the potential to offer accurate and continuous assessment of patients with lung disease. However, privacy preservation becomes a significant issue when it comes to continuous passive audio recording. Various techniques have been employed to obfuscate the speech within audio in these applications. However, that penalizes the accuracy of detection by affecting of-interest, non-speech audio parts. This is inevitably undesirable as it contradicts the notion of sensing. In this paper, we propose a novel algorithm to achieve the goal by employing a machine-learning-based vowel detection algorithm. The algorithm is implemented in a configurable manner to address many different scenarios of data collection. Logistic regression has been utilized to make the algorithm feasible for on-device implementation. We have shown that our algorithm achieves the goals in that the obfuscated speech is unrecognizable while cough sounds are identifiable by symptom detection models as well as a human ear.
AB - Audio-based automated pulmonary symptom detection has the potential to offer accurate and continuous assessment of patients with lung disease. However, privacy preservation becomes a significant issue when it comes to continuous passive audio recording. Various techniques have been employed to obfuscate the speech within audio in these applications. However, that penalizes the accuracy of detection by affecting of-interest, non-speech audio parts. This is inevitably undesirable as it contradicts the notion of sensing. In this paper, we propose a novel algorithm to achieve the goal by employing a machine-learning-based vowel detection algorithm. The algorithm is implemented in a configurable manner to address many different scenarios of data collection. Logistic regression has been utilized to make the algorithm feasible for on-device implementation. We have shown that our algorithm achieves the goals in that the obfuscated speech is unrecognizable while cough sounds are identifiable by symptom detection models as well as a human ear.
UR - https://www.scopus.com/pages/publications/85062529327
U2 - 10.1109/BIBM.2018.8621406
DO - 10.1109/BIBM.2018.8621406
M3 - Conference contribution
AN - SCOPUS:85062529327
T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
SP - 1107
EP - 1112
BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
A2 - Schmidt, Harald
A2 - Griol, David
A2 - Wang, Haiying
A2 - Baumbach, Jan
A2 - Zheng, Huiru
A2 - Callejas, Zoraida
A2 - Hu, Xiaohua
A2 - Dickerson, Julie
A2 - Zhang, Le
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Y2 - 3 December 2018 through 6 December 2018
ER -
