Privacy-preserving cancer type prediction with homomorphic encryption

Author:

Sarkar Esha,Chielle Eduardo,Gursoy Gamze,Chen Leo,Gerstein Mark,Maniatakos Michail

Abstract

AbstractCancer genomics tailors diagnosis and treatment based on an individual’s genetic information and is the crux of precision medicine. However, analysis and maintenance of high volume of genetic mutation data to build a machine learning (ML) model to predict the cancer type is a computationally expensive task and is often outsourced to powerful cloud servers, raising critical privacy concerns for patients’ data. Homomorphic encryption (HE) enables computation on encrypted data, thus, providing cryptographic guarantees to protect privacy. But restrictive overheads of encrypted computation deter its usage. In this work, we explore the challenges of privacy preserving cancer type prediction using a dataset consisting of more than 2 million genetic mutations from 2713 patients for several cancer types by building a highly accurate ML model and then implementing its privacy preserving version in HE. Our solution for cancer type inference encodes somatic mutations based on their impact on the cancer genomes into the feature space and then uses statistical tests for feature selection. We propose a fast matrix multiplication algorithm for HE-based model. Our final model achieves 0.98 micro-average area under curve improving accuracy from 70.08 to 83.61% , being 550 times faster than the standard matrix multiplication-based privacy-preserving models. Our tool can be found at https://github.com/momalab/octal-candet.

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Cited by 15 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Privacy preservation in Artificial Intelligence and Extended Reality (AI-XR) metaverses: A survey;Journal of Network and Computer Applications;2024-11

2. Private pathological assessment via machine learning and homomorphic encryption;BioData Mining;2024-09-10

3. Confidential and Protected Disease Classifier using Fully Homomorphic Encryption;2024 IEEE Conference on Artificial Intelligence (CAI);2024-06-25

4. SparseHE: An Efficient Privacy-Preserving Biomedical Prediction Approach Using Sparse Homomorphic Encryption;2024 IEEE 12th International Conference on Healthcare Informatics (ICHI);2024-06-03

5. Silicon-Proven ASIC Design for the Polynomial Operations of Fully Homomorphic Encryption;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2024-06

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