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Privacy-preserving machine learning with multiple data providers

Version 2 2024-06-06, 03:00
Version 1 2018-09-06, 13:32
journal contribution
posted on 2024-06-06, 03:00 authored by P Li, T Li, H Ye, J Li, X Chen, Y Xiang
© 2018 Elsevier B.V. With the fast development of cloud computing, more and more data storage and computation are moved from the local to the cloud, especially the applications of machine learning and data analytics. However, the cloud servers are run by a third party and cannot be fully trusted by users. As a result, how to perform privacy-preserving machine learning over cloud data from different data providers becomes a challenge. Therefore, in this paper, we propose a novel scheme that protects the data sets of different providers and the data sets of cloud. To protect the privacy requirement of different providers, we use public-key encryption with a double decryption algorithm (DD-PKE) to encrypt their data sets with different public keys. To protect the privacy of data sets on the cloud, we use ϵ-differential privacy. Furthermore, the noises for the ϵ-differential privacy are added by the cloud server, instead of data providers, for different data analytics. Our scheme is proven to be secure in the security model. The experiments also demonstrate the efficiency of our protocol with different classical machine learning algorithms.

History

Journal

Future generation computer systems

Volume

87

Pagination

341-350

Location

Amsterdam, The Netherlands

ISSN

0167-739X

Language

eng

Publication classification

C Journal article, C1 Refereed article in a scholarly journal

Copyright notice

2018, Elsevier

Publisher

Elsevier