Privacy-preserving machine learning with multiple data providers

Ping Li

doi:10.1016/j.future.2018.04.076

Privacy-preserving machine learning with multiple data providers

2018-10-01

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DOI：10.1016/j.future.2018.04.076

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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 is an element of-differential privacy. Furthermore, the noises for the is an element of-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. (C) 2018 Elsevier B.V. All rights reserved.

Keywords:

Differential privacy

Homomorphic encryption

Outsourcing computation

Machine learning

Privacy-preserving machine learning with multiple data providers

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