Sinem Sav

A Reinforcement Learning-based Approach for Dynamic Privacy Protection in Genomic Data Sharing Beacons
Masoud Poorghaffar Aghdam, Sobhan Shukueian Tabrizi, Kerem Ayoz, Erman Ayday, Sinem Sav*, A. Ercument Cicek*
bioRxiv preprint, 2024
Generated Data with Fake Privacy: Hidden Dangers of Fine-tuning Large Language Models on Generated Data
Atilla Akkus, Mingjie Li, Junjie Chu, Michael Backes, Yang Zhang, Sinem Sav
arXiv preprint, 2024
CURE: Privacy-Preserving Split Learning Done Right
Halil Ibrahim Kanpak, Aqsa Shabbir, Esra Genç, Alptekin Küpçü, Sinem Sav
arXiv preprint, 2024
How to Privately Tune Hyperparameters in Federated Learning? Insights from a Benchmark Study
Natalija Mitic, Apostolos Pyrgelis, Sinem Sav
arXiv preprint, 2024

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 Recurrent Neural Networks
Sinem Sav, Abdulrahman Diaa, Apostolos Pyrgelis, Jean-Philippe Bossuat, and Jean-Pierre Hubaux
Proceedings on Privacy Enhancing Technologies (PoPETS 2023)
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)

Client Security Alone Fails in Federated Learning: 2D and 3D Attack Insights
Santhosh Parampottupadam, Ralf Floca, Dimitrios Bounias, Benjamin Hamm, Saikat Roy, Sinem Sav, Maximilian Zenk, Klaus Maier-Hein
MICCAI Student Board EMERGE Workshop: Empowering MEdical image computing & Research through early-career Expertise, 2024
SlytHErin: An Agile Framework for Encrypted Deep Neural Network Inference
Francesco Intoci*, Sinem Sav*, Apostolos Pyrgelis, Jean-Philippe Bossuat, Juan Ramón Troncoso-Pastoriza, and Jean-Pierre Hubaux
5th Workshop on Cloud Security and Privacy (Cloud S&P 2023) co-located with ACNS.

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