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Mitigating Membership Inference in Deep Learning Applications with High Dimensional Genomic Data.

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Deep learning models for cancer genomics pose privacy risks to patient data. This study investigates these risks and evaluates privacy-preserving strategies to protect sensitive health information.

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Area of Science:

  • Medical applications
  • Genomics
  • Artificial intelligence

Background:

  • Deep learning (DL) shows promise in healthcare.
  • Sharing DL models raises privacy concerns about patient data.
  • Membership inference attacks can reveal training data information.

Purpose of the Study:

  • Investigate membership privacy risks in DL models for cancer genomics.
  • Evaluate privacy-preserving strategies for mitigating these risks.

Main Methods:

  • Membership inference attack analysis.
  • Evaluation of privacy-preserving techniques.

Main Results:

  • Deep learning models for cancer genomics present significant membership privacy risks.
  • Certain privacy-preserving strategies can effectively mitigate these risks.

Conclusions:

  • Addressing privacy concerns is crucial for the ethical deployment of DL in cancer genomics.
  • Further research into robust privacy-preserving methods is warranted for secure healthcare AI.