arXiv subjects

Dimitri Van Landuyt

Publications and source records attributed to Dimitri Van Landuyt.

3 recordsLinked to original sources

arXiv2026

A LINDDUN-based Privacy Threat Modeling Framework for GenAI

As generative AI (GenAI) systems become increasingly prevalent across various technological stacks, the question of how such systems handle sensitive and personal data flows becomes increasingly important. Specifically, both the ability to harness and process large swaths of information as well as their stochastic nature raise key concerns related to both security and privacy. Unfortunately, while some of the traditional security threat modeling can effectively identify certain violations, privacy-related issues are often overlooked. To respond to these challenges, we introduce a novel domain-specific privacy threat modeling framework to support the privacy threat analysis of GenAI-based applications. This framework is constructed through a two-pronged approach: (1) a systematic review of the emerging literature on GenAI privacy threats, and (2) a case-driven application to a representative Chatbot system. These efforts yield a foundational GenAI privacy threat modeling framework built on LINDDUN. The new framework affects three out of the seven privacy threat types of LINDDUN and introduces 100 new GenAI examples to the knowledge base. Its effectiveness is validated on an AI Agent system, which demonstrates that a comprehensive privacy analysis can be supported by the new framework.

cs.CR↗

arXiv2025

To See or Not to See: A Privacy Threat Model for Digital Forensics in Crime Investigation

Digital forensics is a cornerstone of modern crime investigations, yet it raises significant privacy concerns due to the collection, processing, and storage of digital evidence. Despite that, privacy threats in digital forensics crime investigations often remain underexplored, thereby leading to potential gaps in forensic practices and regulatory compliance, which may then escalate into harming the freedoms of natural persons. With this clear motivation, the present paper applies the SPADA methodology for threat modelling with the goal of incorporating privacy-oriented threat modelling in digital forensics. As a result, we identify a total of 298 privacy threats that may affect digital forensics processes through crime investigations. Furthermore, we demonstrate an unexplored feature on how SPADA assists in handling domain-dependency during threat elicitation. This yields a second list of privacy threats that are universally applicable to any domain. We then present a comprehensive and systematic privacy threat model for digital forensics in crime investigation. Moreover, we discuss some of the challenges about validating privacy threats in this domain, particularly given the variability of legal frameworks across jurisdictions. We ultimately propose our privacy threat model as a tool for ensuring ethical and legally compliant investigative practices.

cs.CR↗

arXiv2021

A Comprehensive Feature Comparison Study of Open-Source Container Orchestration Frameworks

(1) Background: Container orchestration frameworks provide support for management of complex distributed applications. Different frameworks have emerged only recently, and they have been in constant evolution as new features are being introduced. This reality makes it difficult for practitioners and researchers to maintain a clear view of the technology space. (2) Methods: we present a descriptive feature comparison study of the three most prominent orchestration frameworks: Docker Swarm, Kubernetes, and Mesos, which can be combined with Marathon, Aurora or DC/OS. This study aims at (i) identifying the common and unique features of all frameworks, (ii) comparing these frameworks qualitatively and quantitatively with respect to genericity in terms of supported features, and (iii) investigating the maturity and stability of the frameworks as well as the pioneering nature of each framework by studying the historical evolution of the frameworks on GitHub. (3) Results: (i) we have identified 124 common features and 54 unique features that we divided into a taxonomy of 9 functional aspects and 27 functional sub-aspects. (ii) Kubernetes supports the highest number of accumulated common and unique features for all 9 functional aspects; however, no evidence has been found for significant differences in genericity with Docker Swarm and DC/OS. (iii) Very little feature deprecations have been found and 15 out of 27 sub-aspects have been identified as mature and stable. These are pioneered in descending order by Kubernetes, Mesos, and Marathon. (4) Conclusion: there is a broad and mature foundation that underpins all container orchestration frameworks. Likely areas for further evolution and innovation include system support for improved cluster security and container security, performance isolation of GPU, disk and network resources, and network plugin architectures.

cs.DC↗