ArticleBMC medical genomics2014
Differentially private distributed logistic regression using private and public data.
Article in BMC medical genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care.JCO clinical cancer informatics · 2020Pooled it
- An empirical assessment of differential privacy in real-world observational data: a case-control study of asthma exacerbation in UK Biobank linked with electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Privacy-Preserving Artificial Intelligence Techniques in Biomedicine.Methods of information in medicine · 2022Article
- Privacy protection of medical data in social network.BMC medical informatics and decision making · 2021Article
- The anatomy of a distributed predictive modeling framework: online learning, blockchain network, and consensus algorithm.JAMIA open · 2020Article
- Protecting patient privacy in survival analyses.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Embracing study heterogeneity for finding genetic interactions in large-scale research consortia.Genetic epidemiology · 2020Article
- Privacy Policy and Technology in Biomedical Data Science.Annual review of biomedical data science · 2018Article
- Selecting Optimal Subset to release under Differentially Private M-estimators from Hybrid Datasets.IEEE transactions on knowledge and data engineering · 2018Article
- Efficient differentially private learning improves drug sensitivity prediction.Biology direct · 2018Article
- Secure Multi-pArty Computation Grid LOgistic REgression (SMAC-GLORE).BMC medical informatics and decision making · 2016Article
- COINSTAC: A Privacy Enabled Model and Prototype for Leveraging and Processing Decentralized Brain Imaging Data.Frontiers in neuroscience · 2016Article
- New horizons in translational bioinformatics: TBC 2013.BMC medical genomics · 2014Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundPrivacy protecting is an important issue in medical informatics and differential privacy is a state-of-the-art framework for data privacy research. Differential privacy offers provable privacy against attackers who have auxiliary information, and can be applied to data mining models (for example, logistic regression). However, differentially private methods sometimes introduce too much noise and make outputs less useful. Given available public data in medical research (e.g. from patients who sign open-consent agreements), we can design algorithms that use both public and private data sets to decrease the amount of noise that is introduced. METHODOLOGY: In this paper, we modify the update step in Newton-Raphson method to propose a differentially private distributed logistic regression model based on both public and private data. EXPERIMENTS AND
resultsWe try our algorithm on three different data sets, and show its advantage over: (1) a logistic regression model based solely on public data, and (2) a differentially private distributed logistic regression model based on private data under various scenarios.
conclusionLogistic regression models built with our new algorithm based on both private and public datasets demonstrate better utility than models that trained on private or public datasets alone without sacrificing the rigorous privacy guarantee.
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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.