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June 6, 2016PLoS ONE164 citationsOpen Access

Enhancing Logistic Regression Privacy Using Efficient Distributed Computing Techniques

Supporting Regularized Logistic Regression Privately and Efficiently

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Authors

WLWenfa LiHLHongzhe LiuPYPeng Yang

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Overview

Randomized trial demonstrates privacy protection in collaborative studies, suggesting broad applications in various fields.

Key Points

  • The aim is to enhance privacy in regularized logistic regression for collaborative studies involving human data.
  • Developed a privacy-enhancing method using distributed computing and strong cryptography.
  • Evaluated the solution through extensive empirical assessments across multiple studies.
  • Focused on applications in domains such as genetics and social sciences.
  • The proposed method ensures strong privacy guarantees while maintaining computational efficiency.
  • Empirical evaluations confirm the scalability of the approach for large-scale studies.
  • The solution is applicable across diverse disciplines, including biomedical and network analysis.
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Cite This Study

Li et al. (2016) studied this question.

synapsesocial.com/papers/6a1669d56e98ef9dc84c50bchttps://doi.org/10.1371/journal.pone.0156479
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