Privacy-Preserving Multiparty Learning for Logistic Regression

Du, Wei; Li, Ang; Li, Qinghua

doi:10.1007/978-3-030-01701-9_30

Cited by 15 publications

(9 citation statements)

References 20 publications

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“…Du et al [35] employed the differential privacy mechanism [36] to preserve privacy in multi-party logistic regression model training. The authors approximated the objective function using Taylor's expansion.…”

Section: Related Workmentioning

confidence: 99%

Multi-Party Privacy-Preserving Logistic Regression with Poor Quality Data Filtering for IoT Contributors

Edemacu

Kim

2021

Electronics

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Nowadays, the internet of things (IoT) is used to generate data in several application domains. A logistic regression, which is a standard machine learning algorithm with a wide application range, is built on such data. Nevertheless, building a powerful and effective logistic regression model requires large amounts of data. Thus, collaboration between multiple IoT participants has often been the go-to approach. However, privacy concerns and poor data quality are two challenges that threaten the success of such a setting. Several studies have proposed different methods to address the privacy concern but to the best of our knowledge, little attention has been paid towards addressing the poor data quality problems in the multi-party logistic regression model. Thus, in this study, we propose a multi-party privacy-preserving logistic regression framework with poor quality data filtering for IoT data contributors to address both problems. Specifically, we propose a new metric gradient similarity in a distributed setting that we employ to filter out parameters from data contributors with poor quality data. To solve the privacy challenge, we employ homomorphic encryption. Theoretical analysis and experimental evaluations using real-world datasets demonstrate that our proposed framework is privacy-preserving and robust against poor quality data.

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Section: Related Workmentioning

confidence: 99%

Multi-Party Privacy-Preserving Logistic Regression with Poor Quality Data Filtering for IoT Contributors

Edemacu

Kim

2021

Electronics

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“…The second method is differential privacy, a universally accepted mathematical structure for protecting data privacy. The main application of differential privacy in machine learning is when the model is published publicly after training in a way that personal data points cannot be distinguished from the released model [18][19] [20] [21].…”

Section: Related Workmentioning

confidence: 99%

Privacy-preserving Logistic Regression with Secret Sharing

Ghavamipour¹,

Türkmen²,

Jian³

2021

Preprint

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Background: Logistic regression (LR) is a widely used classification method for modeling binary outcomes in many medical data classification tasks. Research that collects and combines datasets from various data custodians and jurisdictions can excessively benefit from the increased statistical power to support their analyzing goals. However, combining data from these various sources creates significant privacy concerns that need to be addressed. Methods:In this paper, we proposed secret sharing-based privacy-preserving logistic regression protocols using the Newton-Raphson method. Our proposed approaches are based on secure Multi-Party Computation (MPC) with different security settings to analyze data owned by several data holders. Results: We conducted experiments on both synthetic data and real-world datasets and compared the efficiency and accuracy of them with those of an ordinary logistic regression model. Experimental results demonstrate that the proposed protocols are highly efficient and accurate.Conclusions: This study introduces iterative algorithms to simplify the federated training a logistic regression model in a privacy-preserving manner. Our implementation results show that our improved method can handle large datasets used in securely training a logistic regression from multiple sources.

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“…Due to the high effectiveness and efficiency, DP has been extensively applied in general machine learning [30], [36]- [39] as well as federated learning algorithms [7], [9], [31], [32], [40], [41]. When implementing DP in machine learning, Laplace or Gaussian mechanism is usually adopted to add properly calibrated noise according to the global sensitivity of gradient's norm, which, however, is difficult to estimate in many machine learning models, especially the deep learning.…”

Section: Related Workmentioning

confidence: 99%

Asynchronous Federated Learning with Differential Privacy for Edge Intelligence

Li,

Yang,

Ren

et al. 2019

Preprint

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Federated learning has been showing as a promising approach in paving the last mile of artificial intelligence, due to its great potential of solving the data isolation problem in large scale machine learning. Particularly, with consideration of the heterogeneity in practical edge computing systems, asynchronous edge-cloud collaboration based federated learning can further improve the learning efficiency by significantly reducing the straggler effect. Despite no raw data sharing, the open architecture and extensive collaborations of asynchronous federated learning (AFL) still give some malicious participants great opportunities to infer other parties' training data, thus leading to serious concerns of privacy. To achieve a rigorous privacy guarantee with high utility, we investigate to secure asynchronous edge-cloud collaborative federated learning with differential privacy, focusing on the impacts of differential privacy on model convergence of AFL. Formally, we give the first analysis on the model convergence of AFL under DP and propose a multi-stage adjustable private algorithm (MAPA) to improve the trade-off between model utility and privacy by dynamically adjusting both the noise scale and the learning rate. Through extensive simulations and real-world experiments with an edge-could testbed, we demonstrate that MAPA significantly improves both the model accuracy and convergence speed with sufficient privacy guarantee.

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Privacy-Preserving Multiparty Learning for Logistic Regression

Cited by 15 publications

References 20 publications

Multi-Party Privacy-Preserving Logistic Regression with Poor Quality Data Filtering for IoT Contributors

Multi-Party Privacy-Preserving Logistic Regression with Poor Quality Data Filtering for IoT Contributors

Privacy-preserving Logistic Regression with Secret Sharing

Asynchronous Federated Learning with Differential Privacy for Edge Intelligence

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