مقاله انگلیسی رایگان در مورد حفاظت از حریم شخصی با استفاده از چندین ارائه دهنده خدمات – الزویر 2018

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.

Introduction

With the fast development of cloud computing, more and more data and applications are moved from the local to cloud servers, including machine learning and other data analytics. However, the cloud computing platform cannot be fully trusted because it is run by a third party. Cloud users lose the control of their data after outsourcing their data to the cloud. To protect the privacy, the data are usually encrypted before they are uploaded to the cloud storage. However, the encryption techniques render the data Though there are some traditional techniques such as homomorphic cryptographic techniques to provide solutions for the data utilization over encrypted data, they are inefficient in practice. To address this challenge, another important notion of differential privacy has been proposed. It can not only protect the privacy, but also provides efficient data operations. However, most of the previous mainly focus on the data from a single user. It is common that the data always from different data providers for machine learning. Therefore, how to perform machine learning over cloud data from multiple users become a new challenge. Traditional differential privacy technique and encryption methods are not practical for this environment. On one hand, the data from different users are encrypted with different public keys or noises, which makes the computation be difficult. On the other hand, data have to be proceeded in different ways for different applications, which makes both the communication overhead and computation overhead be huge.