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

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.