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CAMDA 2025 - ELSA Health Privacy Challenge

This repository is a "starter package" for the Health Privacy Competition that runs within CAMDA 2025. The Health Privacy Challenge is organized in the context of the European Lighthouse on Safe and Secure AI (ELSA, https://elsa-ai.eu).

The Health Privacy Competition will run in a “Blue Team (🫐) vs Red Team (🍅)” scheme.

  • The blue teams will develop novel privacy preserving generative methods that can mitigate privacy risks while preserving biological insights for gene expression datasets,
  • The red teams will launch trustworthy and realistic membership inference attacks (MIA) against blue teams’ solutions to assess whether these generative methods can withstand privacy attacks.

We are looking forward to engaging with you and working together to deepen our understanding of privacy in healthcare. 🤗

Introduction

This repository contains:

  • 👩‍💻 Baseline code for generative methods (Blue Teams) and Membership Inference Attack algorithms (Red teams).
  • 📝 Documentation that details setup and submission instructions for the competition.
  • 📎 Submission templates to base your submissions on.

Other resources:

🎢 Get started!

Both teams, please check out Getting Started to set up and use the starter package!

Datasets

We re-distribute pre-processed versions of two open-access TCGA RNA-seq datasets, available through the GDC portal:

  • TCGA-BRCA RNASeq

    Dimensions: <1089 x 978> Details: Suitable for cancer subtype prediction (5 subtypes)

  • TCGA COMBINED RNASeq (with 10 different cancer tissues )

    Dimensions: <4323 x 978> Details: Suitable for cancer tissue of origin prediction (10 tissues)

You can download the pre-processed datasets from ELSA Benchmarks Competition platform after registration.

Navigate here for details about the pre-processing steps.

📅 Schedule

Timeline

👥 Organization Team

This competition is designed as a collaborative effort between European Molecular Biology Laboratory (EMBL), CISPA Helmholtz Center for Information Security, and the University of Helsinki with the support of Barcelona Computer Vision Center (CVC) within the context of ELSA Project.

We thank Katharina Mikulik (DKFZ) and Kevin Domanegg (DKFZ) for helpful feedback.

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