مقاله انگلیسی رایگان در مورد حفاظت از حریم شخصی با استفاده از چندین ارائه دهنده خدمات – الزویر ۲۰۱۸

مقاله انگلیسی رایگان در مورد حفاظت از حریم شخصی با استفاده از چندین ارائه دهنده خدمات – الزویر ۲۰۱۸

 

مشخصات مقاله
ترجمه عنوان مقاله حفاظت از حریم شخصی با استفاده از چندین ارائه دهنده خدمات
عنوان انگلیسی مقاله Privacy-preserving machine learning with multiple data providers
انتشار مقاله سال ۲۰۱۸
تعداد صفحات مقاله انگلیسی ۱۰ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله
مقاله پژوهشی (Research article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) scopus – master journals – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۴٫۶۳۹ در سال ۲۰۱۷
شاخص H_index ۸۵ در سال ۲۰۱۸
شاخص SJR ۰٫۸۴۴ در سال ۲۰۱۸
رشته های مرتبط مهندسی کامپیوتر
گرایش های مرتبط امنیت اطلاعات، هوش مصنوعی
نوع ارائه مقاله
ژورنال
مجله / کنفرانس نسل آینده سیستم های کامپیوتری – Future Generation Computer Systems
دانشگاه School of Computer Science – Guangzhou University – China
کلمات کلیدی حریم خصوصی دیفرانسیلی، رمزنگاری هومورفیک، محاسبات برون سپاری، یادگیری ماشین
کلمات کلیدی انگلیسی Differential privacy, Homomorphic encryption, Outsourcing computation, Machine learning
شناسه دیجیتال – doi
https://doi.org/10.1016/j.future.2018.04.076
کد محصول E10208
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Highlights
Abstract
Keywords
۱ Introduction
۲ Related work
۳ Preliminaries
۴ System and adversary models
۵ Our solution
۶ Simulation results
۷ Security analysis
۸ Conclusion and future work
Acknowledgments
References
Vitae

بخشی از متن مقاله:
abstract

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.

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