81 citations · 82 across the 6 of their papers we have counts for
7 papers
The Price of Peeking: Anytime-Valid Leakage Detection on ML-KEM EM Traces
Georgios Feretzakis, Alexandros Papaspyridis
Side-channel evaluators routinely inspect leakage tests while acquisition is still running, and extend or stop the campaign based on what they see. Fixed-horizon screening such as…
Validating Emergency Department Admission Predictions Based on Local Data Through MIMIC-IV
Francesca Meimeti, Loukas Triantafyllopoulos, Aikaterini Sakagianni +7
The effective management of Emergency Department (ED) overcrowding is essential for improving patient outcomes and optimizing healthcare resource allocation. This study validates h…
Trustworthy AI: Securing Sensitive Data in Large Language Models
Georgios Feretzakis, Vassilios S. Verykios
Large Language Models (LLMs) have transformed natural language processing (NLP) by enabling robust text generation and understanding. However, their deployment in sensitive domains…
Using machine learning techniques to predict hospital admission at the emergency department
Georgios Feretzakis, George Karlis, Evangelos Loupelis +10
Introduction: One of the most important tasks in the Emergency Department (ED) is to promptly identify the patients who will benefit from hospital admission. Machine Learning (ML)…
Pulmonary embolism identification in computerized tomography pulmonary angiography scans with deep learning technologies in COVID-19 patients
Chairi Kiourt, Georgios Feretzakis, Konstantinos Dalamarinis +8
The main objective of this work is to utilize state-of-the-art deep learning approaches for the identification of pulmonary embolism in CTPA-Scans for COVID-19 patients, provide an…
On Using Linear Diophantine Equations to Tune the extent of Look Ahead while Hiding Decision Tree Rules
Georgios Feretzakis, Dimitris Kalles, Vassilios S. Verykios
This paper focuses on preserving the privacy of sensitive pat-terns when inducing decision trees. We adopt a record aug-mentation approach for hiding sensitive classification rules…
Data set operations to hide decision tree rules
Dimitris Kalles, Vassilios S. Verykios, Georgios Feretzakis +1
This paper focuses on preserving the privacy of sensitive patterns when inducing decision trees. We adopt a record augmentation approach for hiding sensitive classification rules i…