Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-Sparse Keys. - Jean-Philippe Bossuat; Christian Mouchet; Juan Ramón Troncoso-Pastoriza; Jean-Pierre Hubaux (IACR Cryptol. ePrint Arch., 2020)

POSEIDON: Privacy-Preserving Federated Neural Network Learning. - Sinem Sav; Apostolos Pyrgelis; Juan Ramón Troncoso-Pastoriza; David Froelicher; Jean-Philippe Bossuat; Joao Sa Sousa; Jean-Pierre Hubaux (NDSS, 2021)

Scalable Privacy-Preserving Distributed Learning. - David Froelicher; Juan Ramón Troncoso-Pastoriza; Apostolos Pyrgelis; Sinem Sav; Joao Sa Sousa; Jean-Philippe Bossuat; Jean-Pierre Hubaux (Proc. Priv. Enhancing Technol., 2021)

Multiparty Homomorphic Encryption from Ring-Learning-with-Errors. - Christian Mouchet; Juan Ramón Troncoso-Pastoriza; Jean-Philippe Bossuat; Jean-Pierre Hubaux (Proc. Priv. Enhancing Technol., 2021)

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

Bootstrapping for Approximate Homomorphic Encryption with Negligible Failure-Probability by Using Sparse-Secret Encapsulation. - Jean-Philippe Bossuat; Juan Ramón Troncoso-Pastoriza; Jean-Pierre Hubaux (IACR Cryptol. ePrint Arch., 2022)

Privacy-preserving federated neural network learning for disease-associated cell classification. - Sinem Sav; Jean-Philippe Bossuat; Juan Ramón Troncoso-Pastoriza; Manfred Claassen; Jean-Pierre Hubaux (Patterns, 2022)

slytHErin: An Agile Framework for Encrypted Deep Neural Network Inference. - Francesco Intoci; Sinem Sav; Apostolos Pyrgelis; Jean-Philippe Bossuat; Juan Ramón Troncoso-Pastoriza; Jean-Pierre Hubaux (ACNS Workshops, 2023)

Scalable and Privacy-Preserving Federated Principal Component Analysis. - David Froelicher; Hyunghoon Cho; Manaswitha Edupalli; Joao Sa Sousa; Jean-Philippe Bossuat; Apostolos Pyrgelis; Juan Ramón Troncoso-Pastoriza; Bonnie Berger; Jean-Pierre Hubaux (SP, 2023)

Privacy-Preserving Federated Recurrent Neural Networks. - Sinem Sav; Abdulrahman Diaa; Apostolos Pyrgelis; Jean-Philippe Bossuat; Jean-Pierre Hubaux (Proc. Priv. Enhancing Technol., 2023)

Security Guidelines for Implementing Homomorphic Encryption. - Jean-Philippe Bossuat; Rosario Cammarota; Ilaria Chillotti; Benjamin R. Curtis; Wei Dai 007; Huijing Gong; Erin Hales; Duhyeong Kim; Bryan Kumara; Changmin Lee 001; Xianhui Lu; Carsten Maple; et al. (IACR Commun. Cryptol., 2024)

Security Guidelines for Implementing Homomorphic Encryption. - Jean-Philippe Bossuat; Rosario Cammarota; Jung Hee Cheon; Ilaria Chillotti; Benjamin R. Curtis; Wei Dai 007; Huijing Gong; Erin Hales; Duhyeong Kim; Bryan Kumara; Changmin Lee 001; Xianhui Lu; et al. (IACR Cryptol. ePrint Arch., 2024)

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

Accurate and Composable Noise Estimates for CKKS with Application to Exact HE Computation. - Jean-Philippe Bossuat; Anamaria Costache; Christian Mouchet; Lea Nürnberger; Juan Ramón Troncoso-Pastoriza (IACR Commun. Cryptol., 2025)

TETRIS: Composing FHE Techniques for Private Functional Exploration Over Large Datasets. - Malika Izabachène; Jean-Philippe Bossuat (Proc. Priv. Enhancing Technol., 2025)