Can I read PILLAR: an AI-Powered Privacy Threat Modeling Tool on EtoBox?

PILLAR: an AI-Powered Privacy Threat Modeling Tool by Mollaeefar, Majid; Bissoli, Andrea; Ranise, Silvio is a scholarly article available to read on EtoBox.

What is PILLAR: an AI-Powered Privacy Threat Modeling Tool about?

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, w

Author

Mollaeefar, Majid; Bissoli, Andrea; Ranise, Silvio

Published

2024

Language

EN