Differentially private distributed logistic regression using private and public data

Ji, Zhanglong; Jiang, Xiaoqian; Wang, Shuang; Xiong, Li; Ohno-Machado, Lucila

doi:10.1186/1755-8794-7-s1-s14

Cited by 67 publications

(33 citation statements)

References 21 publications

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“…In section 3.1, the supervised learning module and the feature extraction module of the proposed framework were evaluated using the subspace (ɑ3, ɑ4, ɑ11) of the NSL-KDD data set with binary classes (9,10) only. In a new experiment, the same subspace is again considered; however, the other classes (0,1), (0,5), (1,2), (1,9), (3,5), (3,9), and (6,8) are also studied. In addition, three other subspaces, (ɑ3, ɑ4, ɑ5), (ɑ3, ɑ4, ɑ7), and (ɑ4, ɑ7, ɑ10), are also included in the experiment to study the performance of the proposed analytical framework with DPLR.…”

Section: Multiple Subspace Analysismentioning

confidence: 99%

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Characterization of differentially private logistic regression

Suthaharan

2018

Proceedings of the ACMSE 2018 Conference

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The purpose of this paper is to present an approach that can help data owners select suitable values for the privacy parameter of a differentially private logistic regression (DPLR), whose main intention is to achieve a balance between privacy strength and classification accuracy. The proposed approach implements a supervised learning technique and a feature extraction technique to address this challenging problem and generate solutions. The supervised learning technique selects subspaces from a training data set and generates DPLR classifiers for a range of values of the privacy parameter. The feature extraction technique transforms an original subspace to a differentially private subspace by querying the original subspace multiple times using the DPLR model and the privacy parameter values that were selected by the supervised learning module. The proposed approach then employs a signal processing technique called signal-interference-ratio as a measure to quantify the privacy level of the differentially private subspaces; hence, allows data owner learn the privacy level that the DPLR models can provide for a given subspace and a given classification accuracy.

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Section: Multiple Subspace Analysismentioning

confidence: 99%

“…[6]. Since its introduction, a significant amount of studies have been conducted using this model for achieving privacy strength and prediction/classification accuracy [8,9,12,16]. This is a parametric approach and the selection of its privacy parameter ϵ is a challenging problem.…”

Section: Introductionmentioning

confidence: 99%

Characterization of differentially private logistic regression

Suthaharan

2018

Proceedings of the ACMSE 2018 Conference

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“…This assumption allows the model of uncertainty in predictions. The loss function L( , y) is required to measures the difference between the predicting ̂ of a hypothesis and the expected or true outcome y [13]. The risk associated with the hypothesis h(x) is the expectation of the loss function:…”

Section: Empirical Risk Minimizationmentioning

confidence: 99%

A Logical Approach for Empirical Risk Minimization in Machine Learning for Data Stratification

Taiwo¹,

Awodele²,

Kuyoro³

2017

RJMCS

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The data-driven methods capable of understanding, mimicking and aiding the information processing tasks of Machine Learning (ML) have been applied in an increasing range over the past years in diverse areas at a very high rate, and had achieved great success in predicting and stratifying given data instances of a problem domain. There has been generalization on the performance of the classifier to be the optimal based on the existing performance benchmarks such as accuracy, speed, time to learn, number of features, comprehensibility, robustness, scalability and interpretability. However, these benchmarks alone do not guarantee the successful adoption of an algorithm for prediction and stratification since there may be an incurring risk in its adoption. Therefore, this paper aims at developing a logical approach for using Empirical Risk Minimization (ERM) technique to determine the machine learning classifier with the minimum risk function for data stratification. The generalization on the performance of optimal algorithm was tested on BayesNet, Multilayered perceptron, Projective Adaptive Resonance Theory (PART) and Logistic Model Trees algorithms based on existing performance benchmarks such as correctly classified instances, time to build, kappa statistics, sensitivity and specificity to determine the algorithms with great performances. The study showed that PART and Logistic Model Trees algorithms perform well than others. Hence, a logical approach to apply Empirical Risk Minimization technique on PART and Logistic Model Trees algorithms is shown to give a detailed procedure of determining their empirical risk function to aid the decision of choosing an algorithm to be the best fit classifier for data stratification. This therefore serves as a benchmark for selecting an optimal algorithm for stratification and prediction alongside other benchmarks.

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“…privacy concerns [18]. Recently privacypreserving analysis on distributed datasets has studied in [19,20,21,22,23,24]. Some methods ensure the differential privacy only on private data and several methods take care both public and private data.…”

Section: Introductionmentioning

confidence: 99%

Privacy-preserving quality control of neuroimaging datasets in federated environment

Saha

Calhoun

et al. 2019

Preprint

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Visualization of high dimensional large-scale datasets via an embedding into a 2D map is a powerful exploration tool for assessing latent structure in the data and detecting outliers. It plays a vital role in neuroimaging field because sometimes it is the only way to perform quality control of large dataset. There are many methods developed to perform this task but most of them rely on the assumption that all samples are locally available for the computation. Specifically, one needs access to all the samples in order to compute the distance directly between all pairs of points to measure the similarity.But all pairs of samples may not be available locally always from local sites for various reasons (e.g. privacy concerns for rare disease data, institutional or IRB policies). This is quite common for biomedical data, e.g. neuroimaging and genetic, where privacypreservation is a major concern. In this scenario, a quality control tool that visualizes decentralized dataset in its entirety via global aggregation of local computations is especially important as it would allow screening of samples that cannot be evaluated otherwise. We introduced an algorithm to solve this problem: decentralized data stochastic neighbor embedding (dSNE). In our approach, data samples (i.e. brain images) located at different sites are simultaneously mapped into the same space according to their similarities. Yet, the data never leaves the individual sites and no pairwise metric is ever

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Differentially private distributed logistic regression using private and public data

Cited by 67 publications

References 21 publications

Characterization of differentially private logistic regression

Characterization of differentially private logistic regression

A Logical Approach for Empirical Risk Minimization in Machine Learning for Data Stratification

Privacy-preserving quality control of neuroimaging datasets in federated environment

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