7 citations · 12 across the 8 of their papers we have counts for
8 papers
Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learning
Devharsh Trivedi, Nesrine Kaaniche, Nikos Triandopoulos +2
Collaborative machine learning among financial institutions must be both group-fair and robust against deliberate adversarial manipulation. Existing fairness-aware aggregation meth…
Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning
Adda Akram Bendoukha, Heber Hwang Arcolezi, Nesrine Kaaniche +1
Federated Learning enables collaborative model training across decentralized data sources without data transfer. Averaging-based FL is limited by the presence of non-IID data, whic…
Enabling Multi-Client Authorization in Dynamic SSE
Seydina Ousmane Diallo, Maryline Laurent, Nesrine Kaaniche
Outsourcing encrypted data to the cloud creates a fundamental tension between data privacy and functional searchability. Current Searchable Symmetric Encryption (SSE) solutions fre…
Robust Federated Learning via Byzantine Filtering over Encrypted Updates
Adda Akram Bendoukha, Aymen Boudguiga, Nesrine Kaaniche +3
Federated Learning (FL) aims to train a collaborative model while preserving data privacy. However, the distributed nature of this approach still raises privacy and security issues…
Fair Play for Individuals, Foul Play for Groups? Auditing Anonymization's Impact on ML Fairness
Héber H. Arcolezi, Mina Alishahi, Adda-Akram Bendoukha +1
Machine learning (ML) algorithms are heavily based on the availability of training data, which, depending on the domain, often includes sensitive information about data providers.…
Comparative Analysis of Technical and Legal Frameworks of Various National Digial Identity Solutions
Montassar Naghmouchi, Maryline Laurent, Claire Levallois-Barth +1
National digital identity systems have become a key requirement for easy access to online public services, specially during Covid-19. While many countries have adopted a national d…
SPOT: Secure and Privacy-preserving prOximiTy protocol for e-healthcare systems
Souha Masmoudi, Nesrine Kaaniche, Maryline Laurent
This paper introduces SPOT, a Secure and Privacy-preserving prOximity based protocol for e-healthcare systems. It relies on a distributed proxy-based approach to preserve users' pr…
Label-GCN: An Effective Method for Adding Label Propagation to Graph Convolutional Networks
Claudio Bellei, Hussain Alattas, Nesrine Kaaniche
We show that a modification of the first layer of a Graph Convolutional Network (GCN) can be used to effectively propagate label information across neighbor nodes, for binary and m…