Advancements in Private Set Union: Protocols, Security Frameworks, and Applications

Abstract

Private Set Union (PSU), a subset of Private Set Intersection (PSI) methodologies, is swiftly marking its importance in an era that progressively values computational integrity without infringing data privacy. This necessity sprouted from the intricate balance between ensuring rigorous privacy protection and fostering beneficial data sharing. PSU enables multiple entities to establish a union of their individual datasets without revealing their private information to each other. The foundational security models that drive PSU are both intricate and robust, with roots tracing back to the Computational Diffie-Hellman problems. These problems address the challenges of ensuring secure computations in multi-party scenarios. Attack strategies targeting PSU have evolved over time, leading to an enhancement in the defense mechanisms. Techniques utilizing encryption, randomization, and other cryptographic methods have seen consistent improvement, making privacy-preserved inter-party data analytics both feasible and efficient. Recent breakthroughs in PSU protocols have seen rigorous evaluations based on efficiency, security, and performance metrics. These evaluations aim to determine how these protocols fare under real-world conditions and what challenges they might encounter. Additionally, PSU’s adaptability and relevance have been showcased across various industries. Whether it's digital libraries collaborating for enriched content while preserving copyright restrictions, financial institutions aiming to analyze transaction patterns without compromising individual account details, risk assessment firms sharing data to provide better evaluations without revealing sensitive details, joint graph computations ensuring optimal routing without exposing network infrastructures, or the ever-growing Internet of Things (IoT) ecosystem maintaining device interoperability while upholding user privacy; PSU’s prominence is undeniable.