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Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning
Amr Abourayya, Jens Kleesiek, Kanishka Rao +4
In many critical applications, sensitive data is inherently distributed and cannot be centralized due to privacy concerns. A wide range of federated learning approaches have been p…
Validating GWAS Findings through Reverse Engineering of Contingency Tables
Yuzhou Jiang, Erman Ayday
Reproducibility in genome-wide association studies (GWAS) is crucial for ensuring reliable genomic research outcomes. However, limited access to original genomic datasets (mainly d…
Privacy-Preserving Data Linkage Across Private and Public Datasets for Collaborative Agriculture Research
Osama Zafar, Rosemarie Santa Gonzalez, Gabriel Wilkins +2
Digital agriculture leverages technology to enhance crop yield, disease resilience, and soil health, playing a critical role in agricultural research. However, it raises privacy co…
Privacy-Preserving Collaborative Genomic Research: A Real-Life Deployment and Vision
Zahra Rahmani, Nahal Shahini, Nadav Gat +7
The data revolution holds significant promise for the health sector. Vast amounts of data collected from individuals will be transformed into knowledge, AI models, predictive syste…
AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against Interpretable Models
Abdullah Caglar Oksuz, Anisa Halimi, Erman Ayday
Explainable Artificial Intelligence (XAI) aims to uncover the decision-making processes of AI models. However, the data used for such explanations can pose security and privacy ris…
Privacy-Preserving Optimal Parameter Selection for Collaborative Clustering
Maryam Ghasemian, Erman Ayday
This study investigates the optimal selection of parameters for collaborative clustering while ensuring data privacy. We focus on key clustering algorithms within a collaborative f…