BU-Trace: A Permissionless Mobile System for Privacy-Preserving Intelligent Contact Tracing

Zhe Peng; Jinbin Huang; Haixin Wang; Shihao Wang; Xiaowen Chu; Xinzhi Zhang; Li Chen; Xin Huang; Xiaoyi Fu; Yike Guo; Jianliang Xu

doi:10.1007/978-3-030-73216-5_26

BU-Trace: A Permissionless Mobile System for Privacy-Preserving Intelligent Contact Tracing

Zhe Peng, Jinbin Huang, Haixin Wang, Shihao Wang, Xiaowen Chu, Xinzhi Zhang, Li Chen, Xin Huang, Xiaoyi Fu, Yike Guo, Jianliang Xu^*

^*Corresponding author for this work

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

14 Citations (Scopus)

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has caused an unprecedented health crisis for the global. Digital contact tracing, as a transmission intervention measure, has shown its effectiveness on pandemic control. Despite intensive research on digital contact tracing, existing solutions can hardly meet users’ requirements on privacy and convenience. In this paper, we propose BU - Trace, a novel permissionless mobile system for privacy-preserving intelligent contact tracing based on QR code and NFC technologies. First, a user study is conducted to investigate and quantify the user acceptance of a mobile contact tracing system. Second, a decentralized system is proposed to enable contact tracing while protecting user privacy. Third, an intelligent behavior detection algorithm is designed to ease the use of our system. We implement BU - Trace and conduct extensive experiments in several real-world scenarios. The experimental results show that BU - Trace achieves a privacy-preserving and intelligent mobile system for contact tracing without requesting location or other privacy-related permissions.

Original language	English
Title of host publication	Database Systems for Advanced Applications
Subtitle of host publication	DASFAA 2021 International Workshops - BDQM, GDMA, MLDLDSA, MobiSocial and MUST, Proceedings
Editors	Christian S. Jensen, Ee-Peng Lim, De-Nian Yang, Wang-Chien Lee, Vincent S. Tseng, Vana Kalogeraki, Jen-Wei Huang, Chih-Ya Shen
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	381-397
Number of pages	17
ISBN (Print)	9783030732158
DOIs	https://doi.org/10.1007/978-3-030-73216-5_26
Publication status	Published - 2021
Externally published	Yes
Event	International Conference on Database Systems for Advanced Applications, DASFAA 2021 held in conjunction with BDQM 2021, GDMA 2021, MLDLDSA 2021, MobiSocial 2021 and MUST 2021 - Taipei, Taiwan, Province of China Duration: 11 Apr 2021 → 14 Apr 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12680 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Database Systems for Advanced Applications, DASFAA 2021 held in conjunction with BDQM 2021, GDMA 2021, MLDLDSA 2021, MobiSocial 2021 and MUST 2021
Country/Territory	Taiwan, Province of China
City	Taipei
Period	11/04/21 → 14/04/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Contact tracing
Intelligent
Permissionless
Privacy-preserving

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-73216-5_26

Cite this

Peng, Z., Huang, J., Wang, H., Wang, S., Chu, X., Zhang, X., Chen, L., Huang, X., Fu, X., Guo, Y., & Xu, J. (2021). BU-Trace: A Permissionless Mobile System for Privacy-Preserving Intelligent Contact Tracing. In C. S. Jensen, E.-P. Lim, D.-N. Yang, W.-C. Lee, V. S. Tseng, V. Kalogeraki, J.-W. Huang, & C.-Y. Shen (Eds.), Database Systems for Advanced Applications: DASFAA 2021 International Workshops - BDQM, GDMA, MLDLDSA, MobiSocial and MUST, Proceedings (pp. 381-397). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12680 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-73216-5_26

Peng, Zhe ; Huang, Jinbin ; Wang, Haixin et al. / BU-Trace : A Permissionless Mobile System for Privacy-Preserving Intelligent Contact Tracing. Database Systems for Advanced Applications: DASFAA 2021 International Workshops - BDQM, GDMA, MLDLDSA, MobiSocial and MUST, Proceedings. editor / Christian S. Jensen ; Ee-Peng Lim ; De-Nian Yang ; Wang-Chien Lee ; Vincent S. Tseng ; Vana Kalogeraki ; Jen-Wei Huang ; Chih-Ya Shen. Springer Science and Business Media Deutschland GmbH, 2021. pp. 381-397 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e528b7c8a5874095ac9650d56e0e012e,

title = "BU-Trace: A Permissionless Mobile System for Privacy-Preserving Intelligent Contact Tracing",

abstract = "The coronavirus disease 2019 (COVID-19) pandemic has caused an unprecedented health crisis for the global. Digital contact tracing, as a transmission intervention measure, has shown its effectiveness on pandemic control. Despite intensive research on digital contact tracing, existing solutions can hardly meet users{\textquoteright} requirements on privacy and convenience. In this paper, we propose BU - Trace, a novel permissionless mobile system for privacy-preserving intelligent contact tracing based on QR code and NFC technologies. First, a user study is conducted to investigate and quantify the user acceptance of a mobile contact tracing system. Second, a decentralized system is proposed to enable contact tracing while protecting user privacy. Third, an intelligent behavior detection algorithm is designed to ease the use of our system. We implement BU - Trace and conduct extensive experiments in several real-world scenarios. The experimental results show that BU - Trace achieves a privacy-preserving and intelligent mobile system for contact tracing without requesting location or other privacy-related permissions.",

keywords = "Contact tracing, Intelligent, Permissionless, Privacy-preserving",

author = "Zhe Peng and Jinbin Huang and Haixin Wang and Shihao Wang and Xiaowen Chu and Xinzhi Zhang and Li Chen and Xin Huang and Xiaoyi Fu and Yike Guo and Jianliang Xu",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; International Conference on Database Systems for Advanced Applications, DASFAA 2021 held in conjunction with BDQM 2021, GDMA 2021, MLDLDSA 2021, MobiSocial 2021 and MUST 2021 ; Conference date: 11-04-2021 Through 14-04-2021",

year = "2021",

doi = "10.1007/978-3-030-73216-5\_26",

language = "English",

isbn = "9783030732158",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "381--397",

editor = "Jensen, \{Christian S.\} and Ee-Peng Lim and De-Nian Yang and Wang-Chien Lee and Tseng, \{Vincent S.\} and Vana Kalogeraki and Jen-Wei Huang and Chih-Ya Shen",

booktitle = "Database Systems for Advanced Applications",

}

Peng, Z, Huang, J, Wang, H, Wang, S, Chu, X, Zhang, X, Chen, L, Huang, X, Fu, X, Guo, Y & Xu, J 2021, BU-Trace: A Permissionless Mobile System for Privacy-Preserving Intelligent Contact Tracing. in CS Jensen, E-P Lim, D-N Yang, W-C Lee, VS Tseng, V Kalogeraki, J-W Huang & C-Y Shen (eds), Database Systems for Advanced Applications: DASFAA 2021 International Workshops - BDQM, GDMA, MLDLDSA, MobiSocial and MUST, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12680 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 381-397, International Conference on Database Systems for Advanced Applications, DASFAA 2021 held in conjunction with BDQM 2021, GDMA 2021, MLDLDSA 2021, MobiSocial 2021 and MUST 2021, Taipei, Taiwan, Province of China, 11/04/21. https://doi.org/10.1007/978-3-030-73216-5_26

BU-Trace: A Permissionless Mobile System for Privacy-Preserving Intelligent Contact Tracing. / Peng, Zhe; Huang, Jinbin; Wang, Haixin et al.
Database Systems for Advanced Applications: DASFAA 2021 International Workshops - BDQM, GDMA, MLDLDSA, MobiSocial and MUST, Proceedings. ed. / Christian S. Jensen; Ee-Peng Lim; De-Nian Yang; Wang-Chien Lee; Vincent S. Tseng; Vana Kalogeraki; Jen-Wei Huang; Chih-Ya Shen. Springer Science and Business Media Deutschland GmbH, 2021. p. 381-397 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12680 LNCS).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - BU-Trace

T2 - International Conference on Database Systems for Advanced Applications, DASFAA 2021 held in conjunction with BDQM 2021, GDMA 2021, MLDLDSA 2021, MobiSocial 2021 and MUST 2021

AU - Peng, Zhe

AU - Huang, Jinbin

AU - Wang, Haixin

AU - Wang, Shihao

AU - Chu, Xiaowen

AU - Zhang, Xinzhi

AU - Chen, Li

AU - Huang, Xin

AU - Fu, Xiaoyi

AU - Guo, Yike

AU - Xu, Jianliang

PY - 2021

Y1 - 2021

N2 - The coronavirus disease 2019 (COVID-19) pandemic has caused an unprecedented health crisis for the global. Digital contact tracing, as a transmission intervention measure, has shown its effectiveness on pandemic control. Despite intensive research on digital contact tracing, existing solutions can hardly meet users’ requirements on privacy and convenience. In this paper, we propose BU - Trace, a novel permissionless mobile system for privacy-preserving intelligent contact tracing based on QR code and NFC technologies. First, a user study is conducted to investigate and quantify the user acceptance of a mobile contact tracing system. Second, a decentralized system is proposed to enable contact tracing while protecting user privacy. Third, an intelligent behavior detection algorithm is designed to ease the use of our system. We implement BU - Trace and conduct extensive experiments in several real-world scenarios. The experimental results show that BU - Trace achieves a privacy-preserving and intelligent mobile system for contact tracing without requesting location or other privacy-related permissions.

AB - The coronavirus disease 2019 (COVID-19) pandemic has caused an unprecedented health crisis for the global. Digital contact tracing, as a transmission intervention measure, has shown its effectiveness on pandemic control. Despite intensive research on digital contact tracing, existing solutions can hardly meet users’ requirements on privacy and convenience. In this paper, we propose BU - Trace, a novel permissionless mobile system for privacy-preserving intelligent contact tracing based on QR code and NFC technologies. First, a user study is conducted to investigate and quantify the user acceptance of a mobile contact tracing system. Second, a decentralized system is proposed to enable contact tracing while protecting user privacy. Third, an intelligent behavior detection algorithm is designed to ease the use of our system. We implement BU - Trace and conduct extensive experiments in several real-world scenarios. The experimental results show that BU - Trace achieves a privacy-preserving and intelligent mobile system for contact tracing without requesting location or other privacy-related permissions.

KW - Contact tracing

KW - Intelligent

KW - Permissionless

KW - Privacy-preserving

UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000886740400026

UR - https://openalex.org/W3158307779

UR - https://www.scopus.com/pages/publications/85104752779

U2 - 10.1007/978-3-030-73216-5_26

DO - 10.1007/978-3-030-73216-5_26

M3 - Conference Paper published in a book

SN - 9783030732158

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 381

EP - 397

BT - Database Systems for Advanced Applications

A2 - Jensen, Christian S.

A2 - Lim, Ee-Peng

A2 - Yang, De-Nian

A2 - Lee, Wang-Chien

A2 - Tseng, Vincent S.

A2 - Kalogeraki, Vana

A2 - Huang, Jen-Wei

A2 - Shen, Chih-Ya

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 11 April 2021 through 14 April 2021

ER -

Peng Z, Huang J, Wang H, Wang S, Chu X, Zhang X et al. BU-Trace: A Permissionless Mobile System for Privacy-Preserving Intelligent Contact Tracing. In Jensen CS, Lim EP, Yang DN, Lee WC, Tseng VS, Kalogeraki V, Huang JW, Shen CY, editors, Database Systems for Advanced Applications: DASFAA 2021 International Workshops - BDQM, GDMA, MLDLDSA, MobiSocial and MUST, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 381-397. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-73216-5_26

BU-Trace: A Permissionless Mobile System for Privacy-Preserving Intelligent Contact Tracing

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this