Privacy Protection in Cross-System Data Exchange: A Comprehensive Review of Multi-Party Computation Approaches

Abstract

In today’s era of big data, personal privacy is increasingly at risk due to widespread data sharing. Mobile applications often collect excessive personal information, while advanced analytics can sometimes lead to biased or discriminatory practices. These challenges create an urgent need for secure, privacy-preserving methods that allow sensitive data to be shared and analyzed across multiple parties and diverse systems. This paper reviews the progress made in this area, with a particular focus on the requirements for safe data sharing and controlled dissemination of private information during multi-party data fusion. The review is structured around three main perspectives: privacy-preserving computation, information sharing control, and collaborative secure computation. We begin by examining the current state of privacy protection in large-scale, interconnected environments, followed by a comparison of recent research developments at both national and international levels. In the area of privacy-preserving computation, emerging techniques such as full-lifecycle privacy safeguards, information flow control, and secure data exchange mechanisms are discussed. For information sharing control, three approaches are analyzed—local control, extended control, and desensitization methods. In collaborative secure computation, we outline methods currently being applied in both academic and industry contexts. Finally, the paper highlights key challenges and directions for future research. Traditional approaches such as anonymization, perturbation, and access control, as well as more advanced methods like cryptography and federated learning, all face practical limitations. To achieve robust protection throughout the entire data lifecycle, theoretical models and privacy-aware information systems must be further refined and tailored to different real-world application scenarios.