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Secure tumor classification by shallow neural network using homomorphic encryption.

Seungwan Hong1, Jai Hyun Park2, Wonhee Cho2

  • 1Department of Mathematical Sciences, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea. swanhong@snu.ac.kr.

BMC Genomics
|April 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a privacy-preserving machine learning method for tumor classification using Homomorphic Encryption (HE). The approach enables accurate classification of encrypted genetic data, achieving high performance in a secure genome analysis competition.

Keywords:
Homomorphic encryptionMulti-label classificationNeural networkPrivacySoftmax activation

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Area of Science:

  • Computational biology and bioinformatics
  • Privacy-preserving machine learning
  • Genomic data analysis

Background:

  • Patient privacy is a major concern when applying machine learning to genetic data for tumor classification.
  • Homomorphic Encryption (HE) allows computations on encrypted data, but its limitations hinder direct application of general machine learning algorithms, especially non-polynomial functions like softmax.
  • The iDASH 2020 competition highlighted the need for secure multi-label tumor classification using HE on genetic information.

Purpose of the Study:

  • To develop a secure multi-label tumor classification method using Homomorphic Encryption (HE) that ensures privacy throughout the model inference process.
  • To address the challenge of implementing softmax activation functions within HE schemes for accurate tumor classification.
  • To optimize computational efficiency and data handling for large-scale genetic datasets within a secure HE framework.

Main Methods:

  • Developed a secure multi-label tumor classification model based on a 1-layer neural network with softmax activation, utilizing an approximate HE scheme.
  • Introduced an approximation method to enable softmax activation within HE and implemented efficient data encoding techniques to reduce computational costs.
  • Proposed a HE-friendly data filtering method to manage and reduce the size of large-scale genetic data, optimizing processing for HE.

Main Results:

  • Analyzed The Cancer Genome Atlas (TCGA) dataset comprising 3,622 samples and genetic features from 25,128 genes.
  • Achieved a microAUC of 0.9882 (85% accuracy) by reducing gene features to 4,096 or less using a 1-layer neural network.
  • Successfully performed tumor classification inference on encrypted test data in 3.75 minutes, earning co-first place in the iDASH 2020 competition.

Conclusions:

  • This work represents the first implementation of a neural network model with softmax activation using Homomorphic Encryption (HE).
  • The developed HE optimization methods, including approximation techniques and data filtering, facilitate machine learning implementation and other complex HE applications.
  • The study demonstrates the feasibility of accurate and private tumor classification using HE on sensitive genetic data.