5 citations · 10 across the 6 of their papers we have counts for
14 papers
Mitigating Unintended Memorization with LoRA in Federated Learning for LLMs
Thierry Bossy, Julien Vignoud, Tahseen Rabbani +2
Federated learning (FL) is a popular paradigm for collaborative training which avoids direct data exposure between clients. However, data privacy issues still remain: FL-trained la…
slytHErin: An Agile Framework for Encrypted Deep Neural Network Inference
Francesco Intoci, Sinem Sav, Apostolos Pyrgelis +3
Homomorphic encryption (HE), which allows computations on encrypted data, is an enabling technology for confidential cloud computing. One notable example is privacy-preserving Pred…
Scalable and Privacy-Preserving Federated Principal Component Analysis
David Froelicher, Hyunghoon Cho, Manaswitha Edupalli +6
Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on priv…
Orchestrating Collaborative Cybersecurity: A Secure Framework for Distributed Privacy-Preserving Threat Intelligence Sharing
Juan R. Trocoso-Pastoriza, Alain Mermoud, Romain Bouyé +4
Cyber Threat Intelligence (CTI) sharing is an important activity to reduce information asymmetries between attackers and defenders. However, this activity presents challenges due t…
SoK: Privacy-Preserving Collaborative Tree-based Model Learning
Sylvain Chatel, Apostolos Pyrgelis, Juan Ramon Troncoso-Pastoriza +1
Tree-based models are among the most efficient machine learning techniques for data mining nowadays due to their accuracy, interpretability, and simplicity. The recent orthogonal n…
Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies
Anisa Halimi, Leonard Dervishi, Erman Ayday +5
Providing provenance in scientific workflows is essential for reproducibility and auditability purposes. Workflow systems model and record provenance describing the steps performed…