Get started
Work

Review of privacy computing techniques for multi-party data fusion analysis

Year: 2024
Type: article
Abstract: In the data era, threats to personal privacy information in ubiquitous sharing environments are widespread, such as apps frequently collecting personal information beyond scope, and big data-enabled price discrimination against frequent customers. The need for multi-party privacy computing for cross-system exchanges is urgent. This work focused on the needs of multi-party privacy computing for cross-system exchanges in ubiquitous sharing environments, taking the security sharing and controlled dissemination of private data in multi-party data fusion applications as the starting point, and provided reviews of existing relevant work from the perspectives of multi-party privacy computing, multi-party privacy information sharing control, and multi-party data collaborative secure computing. First, the background and research status of personal privacy information protection in a ubiquitous sharing environment were analyzed. Then, the latest domestic and foreign research results in recent years regarding multi-party privacy computing, multi-party privacy information sharing control, and multi-party data collaborative security computing were reviewed and comparatively analyzed. Regarding multi-party privacy computing, technologies such as full lifecycle privacy protection, privacy information flow control, and secure exchange of sensitive data were introduced. In terms of multi-party privacy information sharing control, localized control, extended control, and anonymization control techniques were discussed. In the aspect of multi-party data collaborative secure computing, commonly used techniques in both academia and industry were discussed. Finally, the challenges and development directions of multi-party privacy computing were prospected. There were still limitations for anonymity, scrambling, or access control-based traditional privacy desensitization measures, cryptography-based measures, and federated learning-based measures, while privacy computing theory provided a computational and information system framework for full-lifecycle protection, which needed to be combined with different application scenarios to implement full-lifecycle privacy information protection.
Authors: LIU Shenglong, HUANG Xiuli, JIANG Yiwen, JIANG Jiawei, TIAN Yuechi, ZHOU Zejun, NIU Ben
Language: English
Open Access status: green
OpenAlex ID: W7052380265
FWCI:
Cites:
Cited by:
Related to: