Privacy-Enhancing Encryption in Data Sharing: A Survey on Security, Performance and Functionality

2026-03-27

AI Summary

This study addresses the multifaceted challenges of security, performance, and functional compatibility in data sharing within “AI+industry” applications. It presents the first unified framework that systematically integrates three major privacy-enhancing technologies—attribute-based encryption (ABE), proxy re-encryption (PRE), and searchable encryption (SE)—along with twelve of their enhanced variants, spanning the entire data-sharing lifecycle. The work further proposes twenty distinct attack models and employs both literature-based statistical analysis and formal modeling to evaluate the efficacy of these techniques across multiple dimensions. By clarifying practical deployment pathways in domains such as smart healthcare and intelligent transportation, the research identifies critical bottlenecks and provides theoretical foundations and future directions for building secure, efficient data-sharing mechanisms.

Abstract

The vigorous development of the Internet has spurred exponential data growth, yet data is predominantly stored in isolated user entities, hampering its full value realization. In large-scale deployment of ``AI+industries'' such as smart medical care, intelligent transportation and smart homes, the gap between data supply and demand continues to widen, and establishing an effective data sharing mechanism is the core of promoting high-quality industrial development. However, data sharing faces significant challenges in security, performance, and functional adaptability. Privacy-enhancing encryption technologies, including Attribute-Based Encryption (ABE), Proxy Re-encryption (PRE), and Searchable Encryption (SE), offer promising solutions with distinct advantages in enhancing security, improving flexibility, and enabling efficient sharing. Statistical analysis of relevant literature from 2020 to 2025 reveals a rising research trend in ABE, PRE and SE, focusing on their data sharing applications. Firstly, this work proposes a data sharing process framework and identifies 20 potential attacks across its stages. Secondly, this work integrates ABE, SE, PRE with 12 enhancement technologies and examines their multi-dimensional impacts on the security, performance, and functional adaptability of data sharing schemes. Lastly, this work outlines key application scenarios, challenges, and future research directions, providing valuable insights for advancing data sharing mechanisms based on privacy-enhancing encryption technologies.

Problem

• data sharing
• privacy-enhancing encryption
• security
• performance
• functional adaptability

Innovation

• Attribute-Based Encryption
• Proxy Re-encryption
• Searchable Encryption
• Privacy-Enhancing Encryption
• Data Sharing Framework