2024/10/11 by Majid Mollaeefar, Mollaeefar, Majid, Andrea Bissoli +3 · 1 voice · 1 citation
Computer Science · Engineering · #Computer science #Computer security #Cryptography and Security (cs.CR) #Digital and Cyber Forensics #Engineering #FOS: Computer and information sciences #Internet privacy #Multiagent Systems (cs.MA) #Network Security and Intrusion Detection #Pillar #Privacy-Preserving Technologies in Data #cs.CR #cs.MA
paper · pdf · doi:10.48550/arxiv.2410.08755
openalex publication_date 2024/10/11 · arxiv published 2024/10/11 · arxiv updated 2024/10/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
The rapid evolution of Large Language Models (LLMs) has unlocked new possibilities for applying artificial intelligence across a wide range of fields, including privacy engineering. As modern applications increasingly handle sensitive user data, safeguarding privacy has become more critical than ever. To protect privacy effectively, potential threats need to be identified and addressed early in the system development process. Frameworks like LINDDUN offer structured approaches for uncovering these risks, but despite their value, they often demand substantial manual effort, expert input, and detailed system knowledge. This makes the process time-consuming and prone to errors. Current privacy threat modeling methods, such as LINDDUN, typically rely on creating and analyzing complex data flow diagrams (DFDs) and system descriptions to pinpoint potential privacy issues. While these approaches are thorough, they can be cumbersome, relying heavily on the precision of the data provided by users. Moreover, they often generate a long list of threats without clear guidance on how to prioritize them, leaving developers unsure of where to focus their efforts. In response to these challenges, we introduce PILLAR (Privacy risk Identification with LINDDUN and LLM Analysis Report), a new tool that integrates LLMs with the LINDDUN framework to streamline and enhance privacy threat modeling. PILLAR automates key parts of the LINDDUN process, such as generating DFDs, classifying threats, and prioritizing risks. By leveraging the capabilities of LLMs, PILLAR can take natural language descriptions of systems and transform them into comprehensive threat models with minimal input from users, reducing the workload on developers and privacy experts while improving the efficiency and accuracy of the process.