7 citations · 12 across the 8 of their papers we have counts for
4 papers · 1 filter
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…