R&D Direction Derivation for Privacy-Enhancing Technologies Through Systematic Patent Analysis

As digital transformation accelerates, the importance of privacy-enhancing technology has increased rapidly. However, existing research and development has proceeded in a fragmented manner without systematic direction setting, showing limitations in practicality and applicability. This study presents a systematic approach combining patent data analysis and OS Matrix methodology to derive concrete and practical R&D directions for promoting privacy-enhancing technologies. To overcome the limitations of existing privacy-enhancing technology classification systems in practical application, this research established a new classification system based on three core principles: proportionality, safety, and reliability. A total of 10,209 privacy-enhancing technology patents from 2005 to 2024 were collected and comprehensively analyzed through CPC/IPC codes, invention titles, and representative claims to derive 54 specific technical objectives. These were combined with 9 detailed means to construct an OS Matrix, and technology competition intensity was classified based on patent distribution. Analysis results showed that among the 486 combinations in the OS Matrix, 172 (35.4%) were identified as blank areas and 194 (39.9%) as low-competition areas. Particularly, combinations such as 'Interoperability Assurance × Multi-Party Computation', 'Process Automation × Zero-Knowledge Proof', and 'Energy Efficiency × Federated Learning' were identified as unexplored areas with no patent applications despite being essential for practical industrial application of privacy-enhancing technologies. When establishing R&D strategies for these blank areas, a preliminary technical verification process is essential to determine whether the corresponding 'means' technology can actually perform the corresponding 'purpose'. This study has significance in that it concretized the R&D direction of privacy-enhancing technologies through data-based objective analysis. By identifying technology development areas urgently required from a practical perspective such as system integration, performance optimization, and regulatory compliance, this research provides practical strategic implications that can contribute to expanding industrial utilization and realizing social value of privacy-enhancing technologies.