Privacy-Preserving Sorting Algorithms Based on Logistic Map for Clouds

Dai, H, Ren, H, Chen, Z, Yang, G and Yi, X 2018, 'Privacy-Preserving Sorting Algorithms Based on Logistic Map for Clouds', Security and Communication Networks, vol. 2018, pp. 1-10.


Document type: Journal Article
Collection: Journal Articles

Title Privacy-Preserving Sorting Algorithms Based on Logistic Map for Clouds
Author(s) Dai, H
Ren, H
Chen, Z
Yang, G
Yi, X
Year 2018
Journal name Security and Communication Networks
Volume number 2018
Start page 1
End page 10
Total pages 10
Publisher John Wiley & Sons
Abstract Outsourcing data in clouds is adopted by more and more companies and individuals due to the profits from data sharing and parallel, elastic, and on-demand computing. However, it forces data owners to lose control of their own data, which causes privacy-preserving problems on sensitive data. Sorting is a common operation in many areas, such as machine learning, service recommendation, and data query. It is a challenge to implement privacy-preserving sorting over encrypted data without leaking privacy of sensitive data. In this paper, we propose privacy-preserving sorting algorithms which are on the basis of the logistic map. Secure comparable codes are constructed by logistic map functions, which can be utilized to compare the corresponding encrypted data items even without knowing their plaintext values. Data owners firstly encrypt their data and generate the corresponding comparable codes and then outsource them to clouds. Cloud servers are capable of sorting the outsourced encrypted data in accordance with their corresponding comparable codes by the proposed privacy-preserving sorting algorithms. Security analysis and experimental results show that the proposed algorithms can protect data privacy, while providing efficient sorting on encrypted data.
Subject Data Encryption
Keyword(s) computing environments
encryption
prediction
framework
generator
chaos
flow
DOI - identifier 10.1155/2018/2373545
Copyright notice Copyright © 2018 Hua Dai et al.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ISSN 1939-0114
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Scopus Citation Count Cited 0 times in Scopus Article
Altmetric details:
Access Statistics: 6 Abstract Views  -  Detailed Statistics
Created: Thu, 21 Feb 2019, 12:10:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us