Sinem Sav

Privacy-Preserving Federated Recurrent Neural Networks
Sinem Sav, Abdulrahman Diaa, Apostolos Pyrgelis, Jean-Philippe Bossuat, and Jean-Pierre Hubaux
Under review, 2022

POSEIDON: Privacy-Preserving Federated Neural Network Learning
Sinem Sav, Apostolos Pyrgelis, Juan R. Troncoso-Pastoriza, David Froelicher, Jean-Philippe Bossuat, Joao Sa Sousa, and Jean-Pierre Hubaux
Network and Distributed Systems Security Symposium (NDSS), 2021
SIMARD: A Simulated Annealing Based RNA Design Algorithm with Quality Pre-Selection Strategies
Sinem Sav, David Hampson, and Herbert H. Tsang
IEEE Symposium Series on Computational Intelligence (SSCI), 2016
Examining the Annealing Schedules for RNA Design Algorithm
Halid Emre Erhan, Sinem Sav, Stas Kalashnikov, and Herbert H. Tsang
IEEE Congress on Evolutionary Computation, July 24-29, 2016
Investigation of Multi-Objective Optimization Criteria for RNA Design
David Hampson, Sinem Sav, and Herbert H. Tsang
IEEE Symposium Series on Computational Intelligence (SSCI), 2016

Privacy-Preserving Federated Neural Network Learning for Disease-Associated Cell Classification
Sinem Sav, Jean-Philippe Bossuat, Juan R. Troncoso-Pastoriza, Manfred Claassen, and Jean-Pierre Hubaux
Patterns, Volume 3, 2022
[Poster]
Scalable Privacy-Preserving Distributed Learning
David Froelicher, Juan R. Troncoso-Pastoriza, Apostolos Pyrgelis, Sinem Sav, Joao Sa Sousa, Jean-Philippe Bossuat, and Jean-Pierre Hubaux
Proceedings on Privacy Enhancing Technologies (PoPETS 2021)

System and method for privacy-preserving distributed training of neural network models on distributed datasets
Sinem Sav, Juan Ramón Troncoso-Pastoriza, Apostolos Pyrgelis, David Froelicher, Joao André Gomes de Sá e Sousa, Jean-Philippe Bossuat, Jean-Pierre Hubaux
2022
System and method for privacy-preserving distributed training of machine learning models on distributed datasets
David Froelicher, Juan Ramón Troncoso-Pastoriza, Apostolos Pyrgelis, Sinem Sav, Joao André Gomes de Sá e Sousa, Jean-Pierre Hubaux, Jean-Philippe Bossuat
2021