Privacy-Preserving AI/ML Application Architectures: Techniques, Trade-offs, and Case Studies

Abstract

Given the widespread adoption and fusion of artificial intelligence (AI) and blockchain technologies, safeguarding privacy has become paramount. These techniques not only ensure the confidentiality of individuals' data but also uphold the integrity and reliability of the information. This study provides an introductory overview of AI and blockchain, elucidating their fusion and subsequent emergence of privacy protection methodologies. It delves into specific application contexts such as data encryption, de-identification, multi-tier distributed ledgers, and k-anonymity techniques. Furthermore, the paper critically assesses five pivotal dimensions of privacy protection systems within AI-blockchain integration: authorization management, access control, data security, network integrity, and scalability. Additionally, it conducts a thorough analysis of existing shortcomings, pinpointing their root causes and proposing corresponding remedies. The study also categorizes and synthesizes privacy protection methodologies based on AI-blockchain application contexts and technical frameworks. In conclusion, it outlines prospective avenues for the evolution of privacy protection technologies stemming from the integration of AI and blockchain, emphasizing the need to enhance efficiency and security for a more holistic safeguarding of privacy.