JOURNAL ARTICLE
MLCT: A multi‐level contact tracing scheme with strong privacy.
Published In: Concurrency & Computation: Practice & Experience, 2023, v. 35, n. 19. P. 1 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: Chen, Peng; Zhang, Jixin; Chen, Jiageng; Meng, Weizhi 3 of 3
Abstract
With the outbreak of Covid‐19, both people's health and the world economy are facing great challenges. Contact tracing scheme based on Bluetooth of smartphones has been regarded as a viable way to mitigate the spread of Covid‐19. The existing schemes mainly belong to the centralized or the decentralized structure, both of which have their own limitations. It is infeasible for the existing schemes to balance the different demands of governments and users for user privacy and tracing efficiency at different periods of the epidemic. In this paper, we propose a hybrid contact tracing scheme named MLCT (multi‐level contact tracing scheme) which is mainly based on short group signature. MLCT provides multiple privacy levels by applying anonymous credential technology and secret sharing technology to desensitize user identity privacy and encounter privacy. Comparing to the previous schemes, MLCT fully considers the different demands of the government, patients, and close contacts for user privacy and tracing efficiency in the different stages of Covid‐19. The experimental results show viability in terms of the required resource from both server and mobile phone perspectives. And the security analysis demonstrates that MLCT can achieve the five targets security goals. It is expected that MLCT can contribute to the design and development of contact tracing schemes. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Concurrency & Computation: Practice & Experience. 2023/08, Vol. 35, Issue 19, p1
- Document Type:Article
- Subject Area:Consumer Health
- Publication Date:2023
- ISSN:15320626
- DOI:10.1002/cpe.6929
- Accession Number:169771475
- Copyright Statement:Copyright of Concurrency & Computation: Practice & Experience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.