ArticleBMC medical genomics2018
Logistic regression model training based on the approximate homomorphic encryption.
Article in BMC medical genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Private detection of relatives in forensic genomics using homomorphic encryption.BMC medical genomics · 2024Article
- Privacy-preserving model evaluation for logistic and linear regression using homomorphically encrypted genotype data.Journal of biomedical informatics · 2024Article
- Collaborative privacy-preserving analysis of oncological data using multiparty homomorphic encryption.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Secure human action recognition by encrypted neural network inference.Nature communications · 2022Article
- Secure tumor classification by shallow neural network using homomorphic encryption.BMC genomics · 2022Article
- Increasing adoption rates at animal shelters: a two-phase approach to predict length of stay and optimal shelter allocation.BMC veterinary research · 2021Article
- High performance logistic regression for privacy-preserving genome analysis.BMC medical genomics · 2021Article
- Web-Based Privacy-Preserving Multicenter Medical Data Analysis Tools Via Threshold Homomorphic Encryption: Design and Development Study.Journal of medical Internet research · 2020Article
- Semi-Parallel logistic regression for GWAS on encrypted data.BMC medical genomics · 2020Article
- Optimized homomorphic encryption solution for secure genome-wide association studies.BMC medical genomics · 2020Article
- Privacy-preserving approximate GWAS computation based on homomorphic encryption.BMC medical genomics · 2020Article
- Achieving GWAS with homomorphic encryption.BMC medical genomics · 2020Article
- Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption.BMC medical genomics · 2020Article
- iDASH secure genome analysis competition 2017.BMC medical genomics · 2018Article
- A Full RNS Variant of Approximate Homomorphic Encryption.Selected areas in cryptography : ... annual international workshop, SAC ... proceedings. SAC (Conference) · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundSecurity concerns have been raised since big data became a prominent tool in data analysis. For instance, many machine learning algorithms aim to generate prediction models using training data which contain sensitive information about individuals. Cryptography community is considering secure computation as a solution for privacy protection. In particular, practical requirements have triggered research on the efficiency of cryptographic primitives.
methodsThis paper presents a method to train a logistic regression model without information leakage. We apply the homomorphic encryption scheme of Cheon et al. (ASIACRYPT 2017) for an efficient arithmetic over real numbers, and devise a new encoding method to reduce storage of encrypted database. In addition, we adapt Nesterov's accelerated gradient method to reduce the number of iterations as well as the computational cost while maintaining the quality of an output classifier.
resultsOur method shows a state-of-the-art performance of homomorphic encryption system in a real-world application. The submission based on this work was selected as the best solution of Track 3 at iDASH privacy and security competition 2017. For example, it took about six minutes to obtain a logistic regression model given the dataset consisting of 1579 samples, each of which has 18 features with a binary outcome variable.
conclusionsWe present a practical solution for outsourcing analysis tools such as logistic regression analysis while preserving the data confidentiality.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.