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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.

Cybersecurity risk management consists of several steps including the selection of appropriate controls to minimize risks. This is a difficult task that requires to search through all possible subsets of a set of available controls and identify those that minimize the risks of all stakeholders. Since stakeholders may have different perceptions of the risks (especially when considering the impact of threats), conflicting goals may arise that require to find the best possible trade-offs among the various needs. In this work, we propose a quantitative and (semi)automated approach to solve this problem based on the well-known notion of Pareto optimality. For validation, we show how a prototype tool based on our approach can assist in the Data Protection Impact Assessment mandated by the General Data Protection Regulation on a simplified but realistic use case scenario. We also evaluate the scalability of the approach by conducting an experimental evaluation with the prototype with encouraging results.

The design of access control mechanisms for healthcare systems is challenging: it must strike the right balance between permissions and restrictions. In this work, we propose a novel approach that is based on the Blockchain technology for storage patient medical data and create an audit logging system able to protect health data from unauthorized modification and access. The proposed method consists of a tree structure: a main chain linked with the patient's identity and one or several Subchains which are used for storing additional critical data (e.g., medical diagnoses or access logs).

Remote attestation protocols are widely used to detect device configuration (e.g., software and/or data) compromise in Internet of Things (IoT) scenarios. Unfortunately, the performances of such protocols are unsatisfactory when dealing with thousands of smart devices. Recently, researchers are focusing on addressing this limitation. The approach is to run attestation in a collective way, with the goal of reducing computation and communication. Despite these advances, current solutions for attestation are still unsatisfactory because of their complex management and strict assumptions concerning the topology (e.g., being time invariant or maintaining a fixed topology). In this paper, we propose PADS, a secure, efficient, and practical protocol for attesting potentially large networks of smart devices with unstructured or dynamic topologies. PADS builds upon the recent concept of non-interactive attestation, by reducing the collective attestation problem into a minimum consensus one. We compare PADS with a state-of-the art collective attestation protocol and validate it by using realistic simulations that show practicality and efficiency. The results confirm the suitability of PADS for low-end devices, and highly unstructured networks., Comment: Submitted to ESORICS 2018

Workflows specify collections of tasks that must be executed under the responsibility or supervision of human users. Workflow management systems and workflow-driven applications need to enforce security policies in the form of access control, specifying which users can execute which tasks, and authorization constraints, such as Separation of Duty, further restricting the execution of tasks at run-time. Enforcing these policies is crucial to avoid frauds and malicious use, but it may lead to situations where a workflow instance cannot be completed without the violation of the policy. The Workflow Satisfiability Problem (WSP) asks whether there exists an assignment of users to tasks in a workflow such that every task is executed and the policy is not violated. The WSP is inherently hard, but solutions to this problem have a practical application in reconciling business compliance and business continuity. Solutions to related problems, such as workflow resiliency (i.e., whether a workflow instance is still satisfiable even in the absence of users), are important to help in policy design. Several variations of the WSP and similar problems have been defined in the literature and there are many solution methods available. In this paper, we survey the work done on these problems in the past 20 years.