Publications

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.

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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.

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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).

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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.

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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.

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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.

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Sinem Sav, Apostolos Pyrgelis, Juan R. Troncoso-Pastoriza, David Froelisher, Jean-Philippe Bossuat, Joao Sa Sousa, Jean-Pierre Hubaux (2022). POSEIDON: Privacy-Preserving Federated Neural Network Learning.

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Jean-Philippe Bossuat, Juan R. Troncoso-Pastoriza, Jean-Pierre Hubaux (2022). Bootstrapping for Approximate Homomorphic Encryption with Negligible Failure-Probability by Using Sparse-Secret Encapsulation. 20th International Conference on Applied Cryptography and Network Security.

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David Froelicher, Juan R. Troncoso-Pastoriza, Apostolos Pyrgelis, Sinem Sav, Joao Sa Sousa, Jean-Philippe Bossuat, Jean-Pierre Hubaux (2021). Scalable Privacy-Preserving Distributed Learning. Privacy Enhancing Technologies Symposium (PETS) 2021.

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Christian Mouchet, Juan R. Troncoso-Pastoriza, Jean-Philippe Bossuat, Jean-Pierre Hubaux (2021). Multiparty Homomorphic Encryption from Ring-Learning-with-Errors. Privacy Enhancing Technologies Symposium (PETS) 2021.

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Jean-Philippe Bossuat, Christian Mouchet, Juan R. Troncoso-Pastoriza, Jean-Pierre Hubaux (2021). Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-sparse Keys. Advances in Cryptology – EUROCRYPT 2021.

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Miran Kim, Arif Ozgun Harmanci, Jean-Philippe Bossuat, Sergiu Carpov, Jung Hee Cheon, Ilaria Chillotti, Wonhee Cho, David Froelicher, Nicolas Gama, Mariya Georgieva, Seungwan Hong, Jean-Pierre Hubaux, Duhyeong Kim, Kristin Lauter, Yiping Ma, Lucila Ohno-Machado, Heidi Sofia, Yongha Son, Yongsoo Song, Juan Troncoso-Pastoriza, Xiaoqian Jiang (2021). Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation. Cell Systems, Volume 12, Issue 11, 17 November 2021, Pages 1108-1120.

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Christian Mouchet, Juan R. Troncoso-Pastoriza, Jean-Philippe Bossuat, Jean-Pierre Hubaux (2020). Multiparty Homomorphic Encryption from Ring-Learning-with-Errors. 8th Workshop on Encrypted Computing & Applied Homomorphic Cryptography (WAHC 2020).

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