arXiv subjects

Sara Ramezanian

Publications and source records attributed to Sara Ramezanian.

7 recordsLinked to original sources

arXiv2026

CourseGraph: Finding overlaps and differences in Computer Science courses across universities

Student mobility programs such as Erasmus+ enable students to take courses at other universities, broadening their academic and cultural horizons. However, this flexibility also leads to a practical challenge: ensuring that students do not take courses elsewhere that substantially overlap with courses in their home curriculum. In this work, we propose CourseGraph, a methodology that automates the evaluation of external courses based on insights obtained from the process followed by curriculum administrators when assessing courses for inclusion in a degree program. Course- Graph extracts information such as course titles, descriptions, and learning outcomes from the course webpage. Then, this information is represented semantically using a BERT-based language model, after which the pair-wise similarity between courses can be computed. This information is then used by a Random Forest classifier to determine whether a candidate course abroad overlaps with a course already contained in the student's curriculum. We evaluate CourseGraph using (1) the Computer Science program at Eindhoven University of Technology, which contains information about courses with substantial overlap, and (2) six approved international programs from students enrolled in the Computer Science program at Lund University, including the corresponding decisions made by a curriculum administrator. The experimental results indicate that CourseGraph provides an effective approach for identifying overlapping courses and supporting curriculum alignment across universities.

cs.AI↗

arXiv2026

CyberBridge: Bridging the Gap Between Cybersecurity Education and Industry

This full research paper presents CyberBridge, a framework that automatically maps cybersecurity job descrip- tions to professional role profiles. As the cybersecurity landscape evolves rapidly, it is difficult for academic programs to align curricula with the competencies expected in practice. CyberBridge addresses this gap by decomposing a given vacancy description into its constituent Knowledge, Skill, and Task (KST) statements, embedding them using a sentence- BERT model, and matching them to the most semantically similar workforce profiles. A key contribution of our approach is its interpretability. Rather than functioning as a black box, CyberBridge enables users to trace recommendations back to the specific competencies driving each match, providing a human- readable justification for the resulting mappings. CyberBridge supports three primary use cases; (1) job recom- mendation, providing students with vacancies and closely match- ing professional roles based on their completed curriculum, (2) market analysis, enabling educators and curriculum developers to analyze which roles are currently in demand, and (3) curriculum planning, assessing which course program best prepares students for in-demand cybersecurity roles. As such, CyberBridge can be used by educational institutions as a practical tool for career guidance and evidence-based curriculum development in cybersecurity education.

cs.CR↗

arXiv2026

Short paper: Models in the dark -- Rectification and erasure under GDPR in ML supply chains

The rights to rectification and erasure, as established under the General Data Protection Regulation (GDPR), are central to protecting individuals' privacy. However, their effective enforcement in machine learning (ML) systems remains challenging. Existing work has largely addressed these rights from either a legal or a technical perspective in isolation and disregards the fact that models are produced in complex supply chains involving multiple actors across development, distribution, and deployment. This paper presents a holistic survey of challenges in implementing the rights to rectification and erasure in ML models. Drawing on academic literature and guidance from data protection authorities, we find that many GDPR requirements cannot yet be technically met in practice. Our findings further suggest that issues arising in ML supply chains are insufficiently addressed in research. To tackle this gap, we introduce the notion of models in the dark -- derived models created further downstream in an ML chain without sufficient transparency or traceability -- and analyse the urgent challenges posed by this phenomenon. By adopting an interdisciplinary perspective, this work contributes to bridging the gap between legal requirements and the technical implementation of data subject rights in ML, ultimately supporting the development of trustworthy artificial intelligence.

cs.LG↗

arXiv2026

CurricuLLM: Designing Personalized and Workforce-Aligned Cybersecurity Curricula Using Fine-Tuned LLMs

The cybersecurity landscape is constantly evolving, driven by increased digitalization and new cybersecurity threats. Cybersecurity programs often fail to equip graduates with skills demanded by the workforce, particularly concerning recent developments in cybersecurity, as curriculum design is costly and labor-intensive. To address this misalignment, we present a novel Large Language Model (LLM)-based framework for automated design and analysis of cybersecurity curricula, called CurricuLLM. Our approach provides three key contributions: (1) automation of personalized curriculum design, (2) a data-driven pipeline aligned with industry demands, and (3) a comprehensive methodology for leveraging fine-tuned LLMs in curriculum development. CurricuLLM utilizes a two-tier approach consisting of PreprocessLM, which standardizes input data, and ClassifyLM, which assigns course content to nine Knowledge Areas in cybersecurity. We systematically evaluated multiple Natural Language Processing (NLP) architectures and fine-tuning strategies, ultimately selecting the Bidirectional Encoder Representations from Transformers (BERT) model as ClassifyLM, fine-tuned on foundational cybersecurity concepts and workforce competencies. We are the first to validate our method with human experts who analyzed real-world cybersecurity curricula and frameworks, motivating that CurricuLLM is an efficient solution to replace labor-intensive curriculum analysis. Moreover, once course content has been classified, it can be integrated with established cybersecurity role-based weights, enabling alignment of the educational program with specific job roles, workforce categories, or general market needs. This lays the foundation for personalized, workforce-aligned cybersecurity curricula that prepare students for the evolving demands in cybersecurity.

cs.CR↗

arXiv2025

Abstraction of Trusted Execution Environments as the Missing Layer for Broad Confidential Computing Adoption: A Systematization of Knowledge

Trusted Execution Environments (TEEs) protect sensitive code and data from the operating system, hypervisor, or other untrusted software. Different solutions exist, each proposing different features. Abstraction layers aim to unify the ecosystem, allowing application developers and system administrators to leverage confidential computing as broadly and efficiently as possible. We start with an overview of representative available TEE technologies. We describe and summarize each TEE ecosystem, classifying them in different categories depending on their main design choices. Then, we propose a systematization of knowledge focusing on different abstraction layers around each design choice. We describe the underlying technologies of each design, as well as the inner workings and features of each abstraction layer. Our study reveals opportunities for improving existing abstraction layer solutions. It also highlights WebAssembly, a promising approach that supports the largest set of features. We close with a discussion on future directions for research, such as how future abstraction layers may evolve and integrate with the confidential computing ecosystem.

cs.CR↗

arXiv2022

A Beyond-5G Authentication and Key Agreement Protocol

The standardized Authentication and Key Agreement protocol for 5G networks (5G AKA) have several security and privacy vulnerabilities. In this paper, we propose a novel authentication and key agreement protocol for 5G and beyond that is compatible with the standardized 5G AKA. Our protocol has several privacy and security properties, e.g., perfect forward secrecy, resistance against linkability attacks, and protection against malicious SNs. Moreover, both the user identity protection and the perfect forward secrecy are handled using Key Encapsulation Mechanisms (KEM), which makes our protocol adaptable to the quantum-safe setting. To analyze the performance of the proposed protocol, we use the post-quantum KEM CRYSTALS-Kyber, recently chosen to be standardized by NIST, and NIST post-quantum Round 4 candidate KEMs. The results for communication and computation costs show that utilizing our protocol is feasible in practice and sometimes outperforms the public-key cryptography used in 5G AKA, i.e., ECIES. We further prove the security of our protocol by utilizing ProVerif.

cs.CR↗

arXiv2021

Multi-party Private Set Operations with an External Decider

A Private Set Operation (PSO) protocol involves at least two parties with their private input sets. The goal of the protocol is for the parties to learn the output of a set operation, i.e. set intersection, on their input sets, without revealing any information about the items that are not in the output set. Commonly, the outcome of the set operation is revealed to parties and no-one else. However, in many application areas of PSO the result of the set operation should be learned by an external participant whom does not have an input set. We call this participant the decider. In this paper, we present new variants of multi-party PSO, where there is a decider who gets the result. All parties expect the decider have a private set. Other parties neither learn this result, nor anything else about this protocol. Moreover, we present a generic solution to the problem of PSO.

cs.CR↗