Privacy Preserving Collaborative Machine Learning

Liu, Zheyuan and Zhang, Rui (2021) Privacy Preserving Collaborative Machine Learning. EAI Endorsed Transactions on Security and Safety, 8 (28). e3. ISSN 2032-9393

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Abstract

Collaborative machine learning is a promising paradigm that allows multiple participants to jointly train a machine learning model without exposing their private datasets to other parties. Although collaborative machine learning is more privacy-friendly compared with conventional machine learning methods, the intermediate model parameters exchanged among different participants in the training process may still reveal sensitive information about participants’ local datasets. In this paper, we introduce a novel privacy-preserving collaborative machine learning mechanism by utilizing two non-colluding servers to perform secure aggregation of the intermediate parameters from participants. Compared with other existing solutions, our solution can achieve the same level of accuracy while incurring significantly lower computational cost.

Item Type: Article
Uncontrolled Keywords: Collaborative Machine Learning, Privacy Preservation, ADMM, Secure Aggregation, Security
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 29 Sep 2021 12:51
Last Modified: 29 Sep 2021 12:51
URI: https://eprints.eudl.eu/id/eprint/7143

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