WebGLORE: a Web service for Grid LOgistic REgression

Jiang, Wenchao; Li, Pinghao; Wang, Shuang; Wu, Yuan; Xue, Ming; Ohno-Machado, Lucila; Jiang, Xiaoqian

doi:10.1093/bioinformatics/btt559

Cited by 36 publications

(37 citation statements)

References 4 publications

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“…Logistic regression with data privacy protection receives significant attentions from researchers in biomedical informatics [16], [29], [32], considering different settings of integrating and sharing data. These works do not use the model of [25] and hence are different from ours.…”

Section: Other Related Workmentioning

confidence: 99%

“…Each data source sends to the server a row in (17), whose size is (n lwe + n d · prec) log 2 q (bits) (16) which is O(d 2 prec) as n lwe and q are fixed as parameters of the scheme.…”

Section: Lwe-based Encryptionmentioning

confidence: 99%

“…It is worth noting that the cost at (20) in ring-LWE case is bigger than (16) in LWE case if n lwe < n d · prec.…”

Section: Communication Cost (Data Source To Server)mentioning

confidence: 99%

“…Each ciphertext size is computed using (16) in which n lwe = 2767, prec = 64, log 2 q = 90. The parameters of (n lwe , q) and Gaussian deviation s = 8.0 are set to gain around 128-bit security with respect to current attacks [5], [20], [21].…”

Section: Concrete Estimation Of Costsmentioning

confidence: 99%

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Privacy-Preserving Logistic Regression with Distributed Data Sources via Homomorphic Encryption

Aono

Hayashi

Phong

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYLogistic regression is a powerful machine learning tool to classify data. When dealing with sensitive or private data, cares are necessary. In this paper, we propose a secure system for privacy-protecting both the training and predicting data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training and predicting in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Indeed, we instantiate our system with Paillier, LWE-based, and ring-LWE-based encryption schemes, highlighting the merits and demerits of each instantiation. Besides examining the costs of computation and communication, we carefully test our system over real datasets to demonstrate its utility.

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Section: Other Related Workmentioning

confidence: 99%

“…Each data source sends to the server a row in (17), whose size is (n lwe + n d · prec) log 2 q (bits) (16) which is O(d 2 prec) as n lwe and q are fixed as parameters of the scheme.…”

Section: Lwe-based Encryptionmentioning

confidence: 99%

“…It is worth noting that the cost at (20) in ring-LWE case is bigger than (16) in LWE case if n lwe < n d · prec.…”

Section: Communication Cost (Data Source To Server)mentioning

confidence: 99%

Section: Concrete Estimation Of Costsmentioning

confidence: 99%

See 2 more Smart Citations

Privacy-Preserving Logistic Regression with Distributed Data Sources via Homomorphic Encryption

Aono

Hayashi

Phong

et al. 2016

IEICE Trans. Inf. & Syst.

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“…Related studies have been published focusing on not only simple analyses such as database queries with very specific inclusion/exclusion criteria but also sophisticated algorithms for prediction analysis including logistic regression [13,14], support vector machine (SVM) [15,16], knearest neighborhood [17], Cox regression [18], and tensor factorization [19]. However, most studies involve restrictive assumptions originating from the requirement that data should be integrated in a matrix format, either common feature events assumption for horizontally-partitioned data or common patient records assumption for verticallypartitioned data.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Predictive Modeling: Harmonization of Contextual Embeddings From Different Sources (Preprint)

Huang¹,

Lee²,

Wang³

et al. 2017

Preprint

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Background: Data sharing has been a big challenge in biomedical informatics due to privacy concerns. Contextual embedding models have demonstrated very strong representative capability to describe medical concepts (and their context), and they have shown promise as an alternative way to support deep learning applications without the need to disclose original data. However, contextual embedding models acquired from individual hospitals cannot be directly combined because their embedding spaces are different and naive pooling renders combined embeddings useless.Objective: We present a novel approach to address these issues to promote sharing representation without sharing data. We can build a global model from representations learned from local private data without sacrificing privacy and synchronize information from multiple sources. Methods:We propose a methodology that harmonizes different local contextual embeddings into a global model. We use Word2Vec to generate contextual embeddings from each source and Procrustes to fuse different vector models into one common space by using a list of corresponding pairs as anchor points. With harmonized embeddings, we performed prediction analysis. Results:We used sequential medical events extracted from the Medical Information Mart for Intensive Care III database to evaluate the proposed methodology in predicting the next likely diagnosis of a new patient using either structured data or unstructured data. Under different experimental scenarios, we confirmed that the global model built from harmonized local models achieves more accurate prediction than local models and global model built from naive pooling.Conclusions: Such aggregation of local models using our unique harmonization can serve as the proxy for a global model, combining information from a wide range of institutions and information sources. It allows information unique to a certain hospital to become available to other sites, increasing the fluidity of information flow in health care.

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Embracing study heterogeneity for finding genetic interactions in large‐scale research consortia

Liu

Huang

Urbanowicz

et al. 2019

Genetic Epidemiology

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Genetic interactions have been recognized as a potentially important contributor to the heritability of complex diseases. Nevertheless, due to small effect sizes and stringent multiple-testing correction, identifying genetic interactions in complex diseases is particularly challenging. To address the above challenges, many genomic research initiatives collaborate to form large-scale consortia and develop open access to enable sharing of genome-wide association study (GWAS) data. Despite the perceived benefits of data sharing from large consortia, a number of practical issues have arisen, such as privacy concerns on individual genomic information and heterogeneous data sources from distributed GWAS databases. In the context of large consortia, we demonstrate that the heterogeneously appearing marginal effects over distributed GWAS databases can offer new insights into genetic interactions for which conventional methods have had limited success. In this paper, we develop a novel two-stage testing procedure, named phylogenY-based effect-size tests for interactions using first 2 moments (YETI2), to detect genetic interactions through both pooled marginal effects, in terms of averaging site-specific marginal effects, and heterogeneity in marginal effects across sites, using a meta-analytic framework. YETI2 can not only be applied to large consortia without shared personal information but also can be used to leverage underlying heterogeneity in marginal effects to prioritize potential genetic interactions. We investigate the performance of YETI2 through simulation studies and apply YETI2 to bladder cancer data from dbGaP. K E Y W O R D Sgenetic interaction, heterogeneity in marginal effects, meta-analysis, privacy-preserving algorithm, two-stage testing procedure *Jason H. Moore, Paul Scheet, and Yong Chen contributed equally to this work.

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WebGLORE: a Web service for Grid LOgistic REgression

Abstract: http://dbmi-engine.ucsd.edu/webglore3/. WebGLORE can be used under the terms of GNU general public license as published by the Free Software Foundation.

Cited by 36 publications

References 4 publications

Privacy-Preserving Logistic Regression with Distributed Data Sources via Homomorphic Encryption

Privacy-Preserving Logistic Regression with Distributed Data Sources via Homomorphic Encryption

Privacy-Preserving Predictive Modeling: Harmonization of Contextual Embeddings From Different Sources (Preprint)

Embracing study heterogeneity for finding genetic interactions in large‐scale research consortia

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