ArticleJMIR medical informatics2018
Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation.
Article in JMIR medical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Privacy-Preserving Prediction of Postoperative Mortality in Multi-Institutional Data: Development and Usability Study.JMIR medical informatics · 2024Article
- Summation-based Private Segmented Membership Test from Threshold-Fully Homomorphic Encryption.Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium · 2024Article
- Privacy-Preserving Artificial Intelligence Techniques in Biomedicine.Methods of information in medicine · 2022Article
- Privacy-preserving federated neural network learning for disease-associated cell classification.Patterns (New York, N.Y.) · 2022Article
- Secure tumor classification by shallow neural network using homomorphic encryption.BMC genomics · 2022Article
- Privacy-preserving logistic regression with secret sharing.BMC medical informatics and decision making · 2022Article
- sPLINK: a hybrid federated tool as a robust alternative to meta-analysis in genome-wide association studies.Genome biology · 2022Article
- Machine Learning for Healthcare Wearable Devices: The Big Picture.Journal of healthcare engineering · 2022Review
- In the Pursuit of Privacy: The Promises and Predicaments of Federated Learning in Healthcare.Frontiers in artificial intelligence · 2021Review
- Web-Based Privacy-Preserving Multicenter Medical Data Analysis Tools Via Threshold Homomorphic Encryption: Design and Development Study.Journal of medical Internet research · 2020Article
- Achieving GWAS with homomorphic encryption.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
- Semi-Parallel logistic regression for GWAS on encrypted data.BMC medical genomics · 2020Article
- Privacy-preserving construction of generalized linear mixed model for biomedical computation.Bioinformatics (Oxford, England) · 2020Article
- Logistic regression model training based on the approximate homomorphic encryption.BMC medical genomics · 2018Article
- Privacy-preserving logistic regression training.BMC medical genomics · 2018Article
- Secure Outsourced Matrix Computation and Application to Neural Networks.Conference on Computer and Communications Security : proceedings of the ... conference on computer and communications security. ACM Conference on Computer and Communications Security · 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
backgroundLearning a model without accessing raw data has been an intriguing idea to security and machine learning researchers for years. In an ideal setting, we want to encrypt sensitive data to store them on a commercial cloud and run certain analyses without ever decrypting the data to preserve privacy. Homomorphic encryption technique is a promising candidate for secure data outsourcing, but it is a very challenging task to support real-world machine learning tasks. Existing frameworks can only handle simplified cases with low-degree polynomials such as linear means classifier and linear discriminative analysis.
objectiveThe goal of this study is to provide a practical support to the mainstream learning models (eg, logistic regression).
methodsWe adapted a novel homomorphic encryption scheme optimized for real numbers computation. We devised (1) the least squares approximation of the logistic function for accuracy and efficiency (ie, reduce computation cost) and (2) new packing and parallelization techniques.
resultsUsing real-world datasets, we evaluated the performance of our model and demonstrated its feasibility in speed and memory consumption. For example, it took approximately 116 minutes to obtain the training model from the homomorphically encrypted Edinburgh dataset. In addition, it gives fairly accurate predictions on the testing dataset.
conclusionsWe present the first homomorphically encrypted logistic regression outsourcing model based on the critical observation that the precision loss of classification models is sufficiently small so that the decision plan stays still.
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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.