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Differentially Private Distributed Online Learning.

Chencheng Li1, Pan Zhou1, Li Xiong2

  • 1School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei 430073, China.

IEEE Transactions on Knowledge and Data Engineering
|October 1, 2019
PubMed
Summary
This summary is machine-generated.

We developed a privacy-preserving distributed online learning framework for big data analytics. This approach protects sensitive data using differential privacy (DP) and offers scalable, efficient computation compared to traditional methods.

Keywords:
Differential privacybig datadistributed optimizationmini-batchonline learningsparse

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Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Big data presents challenges like distribution, velocity, dimensionality, and privacy concerns.
  • Existing analytics methods struggle with these characteristics, especially in distributed environments.
  • Online learning on sensitive data requires robust privacy protection.

Purpose of the Study:

  • To develop a privacy-preserving distributed online learning framework for big data analytics.
  • To address challenges of data distribution, velocity, dimensionality, and privacy.
  • To provide a scalable and computationally efficient solution for secure big data analysis.

Main Methods:

  • Developed a distributed online learning algorithm (DOLA) incorporating differential privacy (DP).
  • Implemented a framework where nodes learn locally and exchange parameters with neighbors.
  • Introduced a sparse DOLA for high-dimensional data and modified versions for practical applications (offline setting, mini-batches).

Main Results:

  • The proposed framework offers rigorous and scalable privacy guarantees.
  • Achieved significantly less computational complexity compared to secure multiparty computation (SMC).
  • Experimental results on real datasets validated the feasibility and effectiveness of the private DOLAs.

Conclusions:

  • The privacy-preserving distributed online learning framework effectively handles big data challenges.
  • The approach provides a strong balance between data privacy, computational efficiency, and analytical utility.
  • This work offers a valuable tool for secure and scalable big data analytics in distributed settings.