Privacy-preserving Distributed Machine Learning via Local Randomization and ADMM Perturbation

With the proliferation of training data, distributed machine learning (DML) is becoming more competent for large-scale learning tasks. However, privacy concerns have to be given priority in DML, since training data may contain sensitive information of users. In this paper, we propose a privacy-prese...

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Bibliographic Details
Main Authors: Wang, Xin, Ishii, Hideaki, Du, Linkang, Cheng, Peng, Chen, Jiming
Format: Article in Journal/Newspaper
Language:unknown
Published: arXiv 2019
Subjects:
Machine Learning cs.LG
Cryptography and Security cs.CR
Distributed, Parallel, and Cluster Computing cs.DC
Multiagent Systems cs.MA
Systems and Control eess.SY
Machine Learning stat.ML
FOS Computer and information sciences
FOS Electrical engineering, electronic engineering, information engineering
DML
Online Access:https://dx.doi.org/10.48550/arxiv.1908.01059
https://arxiv.org/abs/1908.01059
Description
Summary:With the proliferation of training data, distributed machine learning (DML) is becoming more competent for large-scale learning tasks. However, privacy concerns have to be given priority in DML, since training data may contain sensitive information of users. In this paper, we propose a privacy-preserving ADMM-based DML framework with two novel features: First, we remove the assumption commonly made in the literature that the users trust the server collecting their data. Second, the framework provides heterogeneous privacy for users depending on data's sensitive levels and servers' trust degrees. The challenging issue is to keep the accumulation of privacy losses over ADMM iterations minimal. In the proposed framework, a local randomization approach, which is differentially private, is adopted to provide users with self-controlled privacy guarantee for the most sensitive information. Further, the ADMM algorithm is perturbed through a combined noise-adding method, which simultaneously preserves privacy for users' less sensitive information and strengthens the privacy protection of the most sensitive information. We provide detailed analyses on the performance of the trained model according to its generalization error. Finally, we conduct extensive experiments using real-world datasets to validate the theoretical results and evaluate the classification performance of the proposed framework.

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