Reda Bellafqira

Associate Professor, IMT Atlantique — AI Security · Federated Learning · Applied Cryptography

I am an Associate Professor at IMT Atlantique (France), where I conduct research on the security and privacy of machine learning systems. My work focuses on deep neural network watermarking, robust federated learning, and privacy-preserving technologies for sensitive domains such as healthcare and genomics.

📧 [email protected] · Google Scholar · ORCID · DBLP · ResearchGate

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Research Interests

• AI Security — Deep neural network watermarking (white-box and black-box), model authentication, detection of malicious models (Byzantine and backdoor attacks)
• Federated Learning — Robust aggregation, defense in non-IID environments, intellectual property protection of collaboratively trained models
• Privacy-Preserving Technologies — Homomorphic encryption, secure multiparty computation, de-identification of electronic health records
• Healthcare & Genomic Data Security — Privacy-preserving GWAS, secure DNA data storage, watermarking for data traceability

Selected Repositories

Repository	Description	Reference
DICTION	Dynamic and robust white-box watermarking scheme for deep neural networks, protecting model intellectual property against removal and modification attacks.	Applied Sciences, 2025
FedCAM	Detection of malicious models in federated learning environments based on their activation maps, targeting Byzantine and backdoor attacks.	WONS 2024
Histogram Shifting Predictions	Blockchain-enhanced reversible watermarking framework for end-to-end data traceability in federated learning systems.	CSP 2025
Integrity_Control_Paillier	Integrity control of homomorphically encrypted data based on the Paillier cryptosystem.	—

Selected Publications

2025

• DICTION: DynamIC robusT whIte bOx Watermarking Scheme for Deep Neural Networks — Applied Sciences, 15(13)
• RoSe-Mix: Robust and Secure Deep Neural Network Watermarking in Black-Box Settings via Image Mixup — Machine Learning and Knowledge Extraction, 7(2)
• A Blockchain-Enhanced Reversible Watermarking Framework for End-to-End Data Traceability in Federated Learning Systems — CSP 2025
• A Dynamic Sliding Window Encoding for Secured DNA Data Storage Compliant With Biological and Indexing Constraints — IEEE Access, 13
• FedCLEAN: Byzantine Defense by Clustering Errors of Activation Maps in Non-IID Federated Learning Environments — arXiv preprint

2024

• FedCrypt: A Dynamic White-Box Watermarking Scheme for Homomorphic Federated Learning
• A White-Box Watermarking Modulation for Encrypted DNN in Homomorphic Federated Learning — SECRYPT 2024
• FedCAM: Identifying Malicious Models in Federated Learning Environments Conditionally to Their Activation Maps — WONS 2024
• Automatic De-identification of French Electronic Health Records: A Cost-Effective Approach Exploiting Distant Supervision and Deep Learning Models — BMC Medical Informatics and Decision Making, 24(1)
• Secure Extraction of Personal Information from EHR by Federated Machine Learning — MIE 2024

2023 and earlier (selection)

• When Federated Learning Meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection — Machine Learning and Knowledge Extraction, 5(4), 2023
• Robust and Imperceptible Watermarking Scheme for GWAS Data Traceability — IWDW 2022
• Cryptosystem Conversion, Packing and Matrix Processing of Homomorphically Encrypted Data: Application to IoT Devices — IEEE Access, 9, 2021
• Privacy-Preserving Genome-Wide Association Study for Rare Mutations — IEEE Access, 8, 2020

35+ peer-reviewed publications — Full list on Google Scholar

Teaching

• C++ Programming — Advanced programming, data structures, design patterns, UML
• Cryptography — Classical and modern cryptographic techniques, security protocols

Supervision

I supervise PhD students, research engineers, and Master's theses on topics including AI security, federated learning, homomorphic encryption, secure multiparty computation, and image watermarking. I welcome inquiries from motivated students and potential collaborators.

Collaboration

I am open to academic and industrial collaborations on:

• Federated learning security and robustness
• Watermarking of machine learning models and multimedia content
• Privacy-preserving machine learning for healthcare and genomics

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_{Python · C++ · PyTorch · TensorFlow · scikit-learn}