arrow
返回

Secure Data Sharing and Search for Cloud-Edge-Collaborative Storage

delete2020-01-01
delete27
delete
OA
AI
T
Tao Ye
P
Peng Xu
金
金海 (Hai Jin) *
DOI:10.1109/ACCESS.2019.2962600delete
delete原文链接
delete分享
delete收藏
查看原文
摘要

摘要

En 中文
Cloud-edge-collaborative storage (CECS) is a promising framework to process data of the internet of things (IoT). It allows edge servers to process IoT data in real-time and stores them on a cloud server. Hence, it can rapidly respond to the requests of IoT devices, provide a massive volume of cloud storage for IoT data, and conveniently share IoT data with users. However, due to the vulnerability of edge and cloud servers, CECS suffers from the risk of data leakage. Existing secure CECS schemes are secure only if all edge servers are trusted. In other words, if any edge server is compromised, all cloud data (generated by IoT devices) will be leaked. Additionally, it is costly to request expected data from the cloud, which is linear with respect to the number of edge servers. To address the above problems, we propose a new secure data search and sharing scheme for CECS. Our scheme improves the existing secure CECS scheme in the following two ways. First, it enables users to generate a public-and-private key pair and manage private keys by themselves. In contrast, the existing solution requires edge servers to manage users' private keys. Second, it uses searchable public-key encryption to achieve more secure, efficient, and flexible data searching. In terms of security, our scheme ensures the confidentiality of cloud data and secure data sharing and searching and avoids a single point of breakthrough. In terms of performance, the experimental results show that our scheme significantly reduces users' computing costs by delegating most of the cryptographic operations to edge servers. Especially, our scheme reduces the computing and communication overhead for generating a search trapdoor compared with the existing secure CECS scheme.
Keyword:
Cloud-edge-collaborative storage
data sharing
data search
searchable encryption
AI总结

AI总结

对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

IEEE Access 封面图
IEEE Access
IF:
3.6
论文数:
9.8W
被引数:
29.4W

机构

引用论文

引用论文

Generating Searchable Public-Key Ciphertexts With Hidden Structures for Fast Keyword Search
err2015-09-01
err49
PREAI
errXu, Peng; Wu, Qianhong; Wang, Wei; Susilo, Willy; Domingo-Ferrer, Josep; Jin, Hai
err分享
err收藏
err分享
err收藏
Frequency of Screening and SBT Technique Trial - North American Weaning Collaboration (FAST-NAWC): a protocol for a multicenter, factorial randomized trial
err2019-10-11
err0
errOAAI
errK. E. A. Burns; Leena Rizvi; Deborah J. Cook; Andrew J. E. Seely; Bram Rochwerg; Francois Lamontagne; John W. Devlin; Peter Dodek; Michael Mayette; Maged Tanios; Audrey Gouskos; Phyllis Kay; Susan Mitchell; Kenneth C. Kiedrowski; Nicholas S. Hill
err分享
err收藏
学者 查看更多内容