Yulliwas Ameur

Yulliwas Ameur

Teacher-Researcher in Cybersecurity, Efrei (Paris Panthéon-Assas Université). PhD in CS (Cnam, 2023). ARCSI member.

Posts by Collection

portfolio

publications

Secure and non-interactive k-NN classifier using symmetric fully homomorphic encryption

Published in Privacy in Statistical Databases (PSD 2022), Paris - Springer, 2022

A non-interactive privacy-preserving k-NN classifier built on symmetric fully homomorphic encryption.

Recommended citation: Yulliwas Ameur, Rezak Aziz, Vincent Audigier, Samia Bouzefrane. "Secure and non-interactive k-NN classifier using symmetric fully homomorphic encryption." Privacy in Statistical Databases (PSD 2022), Springer, 2022, pp. 142-154. https://hal.science/hal-03933277

Application of Homomorphic Encryption in Machine Learning

Published in In: Emerging Trends in Cybersecurity Applications (Springer), 2023

Survey and practical perspective on homomorphic encryption schemes and tools for privacy-preserving machine learning.

Recommended citation: Yulliwas Ameur, Samia Bouzefrane, Vincent Audigier. "Application of Homomorphic Encryption in Machine Learning." In: Emerging Trends in Cybersecurity Applications, Springer, 2023, pp. 391-410. https://hal.science/hal-03933309

Handling security issues by using homomorphic encryption in multi-cloud environment

Published in ANT 2023 - 14th International Conference on Ambient Systems, Networks and Technologies (Leuven, Belgium) - Procedia Computer Science, vol. 220, 2023

Securing outsourced computation across multiple cloud providers with homomorphic encryption.

Recommended citation: Yulliwas Ameur, Samia Bouzefrane, Le Vinh Thinh. "Handling security issues by using homomorphic encryption in multi-cloud environment." Procedia Computer Science, vol. 220, 2023, pp. 390-397. https://hal.science/hal-03933238

Exploring the Scope of Machine Learning using Homomorphic Encryption in IoT/Cloud (PhD thesis)

Published in HESAM Université - Conservatoire national des arts et métiers (Cnam), Paris, 2023

PhD thesis supervised by Samia Bouzefrane and Vincent Audigier - privacy-preserving ML (k-NN, k-means, differential privacy) over encrypted data in IoT/Cloud.

Recommended citation: Yulliwas Ameur. "Exploring the Scope of Machine Learning using Homomorphic Encryption in IoT/Cloud." PhD thesis, HESAM Universite / Cnam, defended December 18, 2023. https://theses.hal.science/tel-04587371

Enhancing privacy in VANETs through homomorphic encryption in machine learning applications

Published in ANT 2024 - 15th International Conference on Ambient Systems, Networks and Technologies (Hasselt, Belgium) - Procedia Computer Science, vol. 238, 2024

Privacy-preserving machine learning for vehicular ad-hoc networks using homomorphic encryption.

Recommended citation: Yulliwas Ameur, Samia Bouzefrane. "Enhancing privacy in VANETs through homomorphic encryption in machine learning applications." Procedia Computer Science, vol. 238, 2024, pp. 151-158. https://hal.science/hal-04676567

Developing Adaptive Homomorphic Encryption through Exploration of Differential Privacy

Published in Journal of Cyber Security and Mobility, 2024

Combining homomorphic encryption with differential privacy for adaptive privacy-preserving machine learning.

Recommended citation: Yulliwas Ameur, Samia Bouzefrane, Soumya Banerjee. "Developing Adaptive Homomorphic Encryption through Exploration of Differential Privacy." Journal of Cyber Security and Mobility, 2024, pp. 863-886. https://hal.science/hal-05330716

Advancing Blockchain Privacy: The Role of Homomorphic Encryption

Published in In: Intelligent Cybersecurity and Resilience for Critical Industries, 2025

Homomorphic encryption as a building block for privacy-preserving blockchain applications.

Recommended citation: Yulliwas Ameur, Ilhem Taberkane, Samia Bouzefrane. "Advancing Blockchain Privacy: The Role of Homomorphic Encryption." In: Intelligent Cybersecurity and Resilience for Critical Industries, 2025. https://hal.science/hal-05330722

Artificial Intelligence and Machine Learning: Revolutionizing Supply Chain Security

Published in In: Securing the Digital Supply Chain: Advances, Challenges, and Solutions (Springer, Cham), 2026

How AI/ML enable predictive analytics, anomaly detection and real-time decision-making for supply chain security.

Recommended citation: Yulliwas Ameur, Malek Kraiem. "Artificial Intelligence and Machine Learning: Revolutionizing Supply Chain Security." In: B. Hammi, N. El Madhoun (eds.), Securing the Digital Supply Chain, Signals and Communication Technology, Springer, Cham, 2026, pp. 183-201. https://doi.org/10.1007/978-3-032-11119-7_8

Secure k-means Clustering using Homomorphic Encryption

Published in ANT 2026 - 17th International Conference on Ambient Systems, Networks and Technologies (Istanbul, Türkiye), 2026

Privacy-preserving k-means clustering on homomorphically encrypted data. To appear in Procedia Computer Science.

Recommended citation: Yulliwas Ameur, Rezak Aziz, Vincent Audigier, Samia Bouzefrane. "Secure k-means Clustering using Homomorphic Encryption." ANT 2026, Istanbul, April 14-16, 2026. To appear in Procedia Computer Science.

talks

teaching

Adjunct lecturer (2023-2024): Paris Cité, Paris 8, ESILV, Guardia, EPSI

Bachelor / MSc modules, Multiple institutions, 2023

Université Paris Cité: digital forensics & malware analysis, introduction to cryptography (M1). Université Paris 8: smart-card programming (M1). ESILV: information security, applied cryptography, security policies, forensics (B3). Guardia Cyber School: IS & cybersecurity fundamentals, intelligence analysis, web attacks, security & civil liberties, transition to post-quantum cryptography. EPSI: SIEM (M1).

Cybersecurity courses - Efrei (since 2024)

Engineering cycle / MSc level, Efrei, Paris Panthéon-Assas Université, 2024

Head of the work-study “Networks & Security” major and educational lead of the Airbus cyber range.