Privacy-Preserving Multikey Computing Framework for Encrypted Data in the Cloud

Privacy-Preserving Computing Framework for Encrypted Data Under Multiple Keys

Lecture Notes in Computer Science - Smart Computing and Communication ◽

10.1007/978-3-030-74717-6_23 ◽

2021 ◽

pp. 215-225

Author(s):

Jun Zhang ◽

Zoe L. Jiang ◽

Ping Li ◽

Siu Ming Yiu

Keyword(s):

Privacy Preserving ◽

Encrypted Data ◽

Computing Framework ◽

Multiple Keys

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An Efficient Privacy-Preserving Rank Query over Encrypted Data in Cloud Computing

Chinese Journal of Computers ◽

10.3724/sp.j.1016.2012.02215 ◽

2012 ◽

Vol 35 (11) ◽

pp. 2215 ◽

Cited By ~ 2

Author(s):

Fang-Quan CHENG ◽

Zhi-Yong PENG ◽

Wei SONG ◽

Shu-Lin WANG ◽

Yi-Hui CUI

Keyword(s):

Cloud Computing ◽

Privacy Preserving ◽

Encrypted Data

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Privacy-Preserving Deep Neural Network Methods: Computational and Perceptual Methods—An Overview

Electronics ◽

10.3390/electronics10111367 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1367

Author(s):

Raghida El El Saj ◽

Ehsan Sedgh Sedgh Gooya ◽

Ayman Alfalou ◽

Mohamad Khalil

Keyword(s):

Neural Networks ◽

Classification Accuracy ◽

Deep Neural Networks ◽

Privacy Preserving ◽

Sensitive Data ◽

Encrypted Data ◽

High Classification Accuracy ◽

Hide Information ◽

Network Methods ◽

Cloud Environments

Privacy-preserving deep neural networks have become essential and have attracted the attention of many researchers due to the need to maintain the privacy and the confidentiality of personal and sensitive data. The importance of privacy-preserving networks has increased with the widespread use of neural networks as a service in unsecured cloud environments. Different methods have been proposed and developed to solve the privacy-preserving problem using deep neural networks on encrypted data. In this article, we reviewed some of the most relevant and well-known computational and perceptual image encryption methods. These methods as well as their results have been presented, compared, and the conditions of their use, the durability and robustness of some of them against attacks, have been discussed. Some of the mentioned methods have demonstrated an ability to hide information and make it difficult for adversaries to retrieve it while maintaining high classification accuracy. Based on the obtained results, it was suggested to develop and use some of the cited privacy-preserving methods in applications other than classification.

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Achieving Efficient and Privacy-Preserving Set Containment Search over Encrypted Data

IEEE Transactions on Services Computing ◽

10.1109/tsc.2021.3065240 ◽

2021 ◽

pp. 1-1

Author(s):

Yandong Zheng ◽

Rongxing Lu ◽

Yunguo Guan ◽

Jun Shao ◽

Hui Zhu

Keyword(s):

Privacy Preserving ◽

Encrypted Data ◽

Set Containment

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Privacy-Preserving Sorting Algorithms Based on Logistic Map for Clouds

Security and Communication Networks ◽

10.1155/2018/2373545 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10

Author(s):

Hua Dai ◽

Hui Ren ◽

Zhiye Chen ◽

Geng Yang ◽

Xun Yi

Keyword(s):

Data Privacy ◽

Logistic Map ◽

Security Analysis ◽

Privacy Preserving ◽

Service Recommendation ◽

Sensitive Data ◽

Encrypted Data ◽

Sorting Algorithms ◽

Common Operation ◽

Cloud Servers

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.

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