Publications

Jean-Philippe Bossuat, Malika Izabachene (2025). Large Domain Homomorphic Evaluation for BFV-like schemes via Ring Repacking. Designs, Codes and Cryptography.

Jean-Philippe Bossuat, Malika Izabachene (2025). TETRIS: Composing FHE Techniques for Private Functional Exploration Over Large Datasets. PoPETS 2025.

Jean-Philippe Bossuat, Anamaria Costache, Christian Mouchet, Lea Nürnberger, Juan Ramón Troncoso-Pastoriza (2025). Accurate and Composable Noise Estimates for CKKS with Application to Exact HE Computation. Communications in Cryptology, Volume 2, Issue 8.

Jean-Philippe Bossuat, Anamaria Costache, Christian Mouchet, Lea Nürnberger, Juan Ramón Troncoso-Pastoriza (2024). Practical q-IND-CPA-D-Secure Approximate Homomorphic Encryption.

Jean-Philippe Bossuat, Rosario Cammarota, Jung Hee Cheon, Ilaria Chillotti, Benjamin R. Curtis, Wei Dai, Huijing Gong, Erin Hales, Duhyeong Kim, Bryan Kumara, Changmin Lee, Xianhui Lu, Carsten Maple, Alberto Pedrouzo-Ulloa, Rachel Player, Luis Antonio Ruiz Lopez, Yongsoo Song, Donggeon Yhee, Bahattin Yildiz (2024). Security Guidelines for Implementing Homomorphic Encryption.

David Froelicher, Hyunghoon Cho, Manaswitha Edupalli, Joao Sa Sousa, Jean-Philippe Bossuat, Apostolos Pyrgelis, Juan R. Troncoso-Pastoriza, Jean-Pierre Hubaux (2023). Scalable and Privacy-Preserving Federated Principal Component Analysis. 2023 IEEE Symposium on Security and Privacy.

Sinem Sav, Abdulrahman Diaa, Apostolos Pyrgelis, Jean-Philippe Bossuat, Jean-Pierre Hubaux (2022). Privacy-Preserving Federated Recurrent Neural Networks. Accepted for publication at the 23rd Privacy Enhancing Technologies Symposium (PETS 2023).

Juan R. Trocoso-Pastoriza, Alain Mermoud, Romain Bouyé, Francesco Marino, Jean-Philippe Bossuat, Vincent Lenders, Jean-Pierre Hubaux (2022). Orchestrating Collaborative Cybersecurity: A Secure Framework for Distributed Privacy-Preserving Threat Intelligence Sharing.

Francesco Intoci, Sinem Sav, Apostolos Pyrgelis, Jean-Philippe Bossuat, Juan R. Troncoso-Pastoriza, Jean-Pierre Hubaux (2022). slytHErin: An Agile Framework for Encrypted Deep Neural Network Inference.

Sinem Sav, Jean-Philippe Bossuat, Juan R. Troncoso-Pastoriza, Manfred Claassen, Jean-Pierre Hubaux (2022). Privacy-preserving federated neural network learning for disease-associated cell classification. Patterns, Volume 3, Issue 5, 13 May 2022.