Dimitra Georgiou

In today’s world of rapidly evolving internet communication, email remains a widely used medium for direct message exchange across ordinary users, organizations, and defense-related environments, where spam campaigns can threaten operational continuity and communication integrity. Despite significant advances in spam filtering techniques using deep learning approaches, comparatively less attention has been given to the robustness of traditional machine learning classifiers under cross-corpus distribution shift. This study benchmarks five classical classifiers—Naive Bayes, Logistic Regression, Random Forest, Linear Support Vector Machine (SVM), and XGBoost—for spam email detection. A unified framework incorporating text preprocessing, TF-IDF feature extraction, engineered metadata features, stratified 10-fold cross-validation, and cross-corpus evaluation was applied to the sampled public email corpora, including one LLM-generated synthetic corpus (Dataset C). Classifier performance was assessed using standard classification metrics, dependence-aware statistical testing, retention-based analysis, and confusion-matrix evaluation. Corpus characteristics were examined using lexical diversity, vocabulary overlap, TF-IDF sparsity, and metadata distribution analyses. Results showed that Linear SVM achieved the highest within-corpus F1-score in the sampled conventional corpora (0.9868 on Dataset A), but this ranking did not consistently carry over to the tested transfers, where performance varied by classifier, feature set, and direction. XGBoost with combined features achieved an F1-score of 0.9233 in the A-to-C transfer, while Linear SVM achieved 0.9437 in B-to-C; these results do not establish general model rankings. Metadata-only scores above the within-corpus baseline in some transfers were consistent with the target class composition, including all-spam predictions, and do not establish a transferable metadata effect. The findings are limited to the sampled corpora and experimental settings; transfer to Dataset C does not establish generalization to LLM-generated spam more broadly.

Introduction: Advances in Information Technology are transforming healthcare systems, with a focus on improving accessibility, efficiency, resilience, and service quality. Wearable devices such as smartwatches and mental health trackers enable continuous biometric data collection, offering significant potential to enhance chronic disorder treatment and overall healthcare quality. However, these technologies introduce critical security and privacy risks, as they handle sensitive patient data.Methods: To address these challenges, this paper proposes a security-by-design cloud-based architecture that leverages wearable body sensors for continuous patient monitoring and mental disorder prediction. The system integrates an Elasticsearch-powered backend to manage biometric data securely. A dedicated framework was developed to ensure confidentiality, integrity, and availability (CIA) of patient data through secure communication protocols and privacy-preserving mechanisms.Results: The proposed architecture successfully enables secure real-time biometric monitoring and data processing from wearable devices. The system is designed to operate 24/7, ensuring robust performance in continuously tracking both mental and physiological health indicators. The inclusion of Elasticsearch provides scalable and efficient data indexing and retrieval, supporting timely healthcare decisions.Discussion: This work addresses key security and privacy challenges inherent in continuous biometric data collection. By incorporating a security-by-design approach, the proposed framework enhances trustworthiness in healthcare monitoring technologies. The solution demonstrates the feasibility of balancing real-time health monitoring needs with stringent data protection requirements.

The General Data Protection Regulation is the core instrument of the reformed legal framework for personal data protection in the European Union. The GDPR was put into effect on May 25, 2018, and requires assessing and conducting a Data Protection Impact Assessment for processing operations that are likely to result in a high risk to the rights and freedoms of natural persons, specifically using new technologies and considering the nature, scope, context, and purposes of the processing. Although GDPR does not precisely specify the types of processing activities for which a DPIA would be necessary, through the guidelines that it provides, the organization should conduct a DPIA, if there is large scale processing of health data. An example of this, is a Cloud-based Health Organization. Taking into account this parameter, that Cloud-based Health Organization processes personal data that could impact the freedoms and rights of a data subject under the GDPR and that the GDPR does not specify a DPIA process to follow, instead it allows organizations to use a framework that complements their existing processes, this paper presents the last two steps of a DPIA study for a Cloud-based Health Organization and provides guidelines on how to carry them out effectively. This study is part of a project for the compliance of Cloud-based Health Organizations with the General Data Protection Regulation 2016/679. For fulfilling the objectives of this study, the PIA-CNIL methodology is applied, which is in accordance with the data privacy impact assessment that has been described in ISO/IEC 29134. The main contribution of this work is the development of a guide that is designed to help Cloud-based Health organizations identify, analyze and reduce data protection risks in relation to their processing activities. More analytically, this research presents the risks that could be materialized by the data processing activities carried out by a Cloud-based Health Organization regarding its Processing Activities and could have an impact on the fundamental rights and freedoms of natural persons.

The General Data Protection Regulation (GDPR) harmonizes personal data protection laws across the European Union, affecting all sectors including the healthcare industry. For processing operations that pose a high risk for data subjects, a Data Protection Impact Assessment (DPIA) is mandatory from May 2018. Taking into account the criticality of the process and the importance of its results, for the protection of the patients’ health data, as well as the complexity involved and the lack of past experience in applying such methodologies in healthcare environments, this paper presents the main steps of a DPIA study and provides guidelines on how to carry them out effectively. To this respect, the Privacy Impact Assessment, Commission Nationale de l’Informatique et des Libertés (PIA-CNIL) methodology has been employed, which is also compliant with the privacy impact assessment tasks described in ISO/IEC 29134:2017. The work presented in this paper focuses on the first two steps of the DPIA methodology and more specifically on the identification of the Purposes of Processing and of the data categories involved in each of them, as well as on the evaluation of the organization’s GDPR compliance level and of the gaps (Gap Analysis) that must be filled-in. The main contribution of this work is the identification of the main organizational and legal requirements that must be fulfilled by the health care organization. This research sets the legal grounds for data processing, according to the GDPR and is highly relevant to any processing of personal data, as it helps to structure the process, as well as be aware of data protection issues and the relevant legislation.

Currently, there are several challenges that cloud-based healthcare systems around the world are facing. The most important issue is to ensure security and privacy, or in other words, to ensure the confidentiality, integrity, and availability of the data. Although the main provisions for data security and privacy were present in the former legal framework for the protection of personal data, the General Data Protection Regulation (GDPR) introduces new concepts and new requirements. In this paper, we present the main changes and the key challenges of the GDPR and, at the same time, we present how a cloud-based security policy could be modified in order to be compliant with the GDPR, as well as how cloud environments can assist developers to build secure and GDPR compliant cloud-based healthcare systems. The major concept of this paper is dual-purpose; primarily, to facilitate cloud providers in comprehending the framework of the new GDPR and secondly, to identify security measures and security policy rules, for the protection of sensitive data in a cloud-based healthcare system, following our risk-based security policy methodology that assesses the associated security risks and takes into account different requirements from patients, hospitals, and various other professional and organizational actors.

Currently, there are several challenges that Cloud-based health-care Systems, around the world, are facing. The most important issue is to ensure security and privacy or in other words to ensure the confidentiality, integrity and availability of the data. Although the main provisions for data security and privacy were present in the former legal framework for the protection of personal data, the General Data Protection Regulation (GDPR) introduces new concepts and new requirements. In this paper, we present the main changes and the key challenges of the General Data Protection Regulation, and also at the same time we present how the Cloud-based Security Policy methodology proposed in [1] could be modified in order to be compliant with the GDPR and how Cloud environments can assist developers to build secure and GDPR compliant Cloud-based health Systems. The major concept of this paper is, primarily, to facilitate Cloud Providers in comprehending the framework of the new General Data Protection Regulation and secondly, to identify security measures and security policy rules for the protection of sensitive data in a Cloud-based Health System, following our risk-based Security Policy Methodology that assesses the associated security risks and takes into account different requirements from patients, hospitals, and various other professional and organizational actors.