Supporting Regularized Logistic Regression Privately and Efficiently

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulatio...

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Veröffentlicht in:PloS one Jg. 11; H. 6; S. e0156479
Hauptverfasser: Li, Wenfa, Liu, Hongzhe, Yang, Peng, Xie, Wei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 06.06.2016
Public Library of Science (PLoS)
Schlagworte:
Analysis
Biology and Life Sciences
Collaboration
Computer and Information Sciences
Computer Communication Networks - organization & administration
Computer Communication Networks - standards
Computer Communication Networks - statistics & numerical data
Computer engineering
Computer Security
Confidentiality
Consortia
Cooperative Behavior
Cryptography
Cybersecurity
Data collection
Data processing
Data retrieval
Distributed processing
Empirical analysis
Engineering and Technology
Epidemiology
Genetic Information Nondiscrimination Act 2008-US
Genetics
Genomes
Health informatics
Humans
Information Dissemination - methods
Information management
Information services
Information sharing
Information technology
Laboratories
Learning algorithms
Linear algebra
Logistic Models
Machine learning
Machine Learning - standards
Management
Mathematical models
Medicine and Health Sciences
Methods
Models, Statistical
Network analysis
Parkinson's disease
Physical Sciences
Privacy
Quantitative genetics
Regression analysis
Regression models
Regularization
Research and Analysis Methods
Security
Smart grid
Social Sciences
Statistical analysis
Statistical models
United States
Beijing China
China
ISSN:1932-6203, 1932-6203
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Zusammenfassung:As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.
Bibliographie:ObjectType-Article-1
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Conceived and designed the experiments: WL WX. Performed the experiments: WL WX. Analyzed the data: WL HL PY WX. Contributed reagents/materials/analysis tools: WL HL PY WX. Wrote the paper: WL HL PY WX.
Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0156479