Privacy-Preserving Ranked Multi-keyword Fuzzy Search on Cloud Encrypted Data Supporting Range Query

2015 ◽  
Vol 40 (8) ◽  
pp. 2375-2388 ◽  
Author(s):  
Jie Wang ◽  
Xiao Yu ◽  
Ming Zhao
Keyword(s):  
Range Query ◽  
Privacy Preserving ◽  
Encrypted Data ◽  
Fuzzy Search

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

2012 ◽  
Vol 35 (11) ◽  
pp. 2215 ◽  
Author(s):  
Fang-Quan CHENG ◽  
Zhi-Yong PENG ◽  
Wei SONG ◽  
Shu-Lin WANG ◽  
Yi-Hui CUI
Keyword(s):  
Cloud Computing ◽  
Privacy Preserving ◽  
Encrypted Data

Location Privacy-Preserving Task Recommendation with Geometric Range Query in Mobile Crowdsensing

2021 ◽  
pp. 1-1
Author(s):  
Chuan Zhang ◽  
Liehuang Zhu ◽  
Chang Xu ◽  
Jianbing Ni ◽  
Cheng Huang ◽  
...  
Keyword(s):  
Location Privacy ◽  
Range Query ◽  
Privacy Preserving ◽  
Mobile Crowdsensing

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

Electronics ◽  
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.

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

2021 ◽  
pp. 1-1
Author(s):  
Yandong Zheng ◽  
Rongxing Lu ◽  
Yunguo Guan ◽  
Jun Shao ◽  
Hui Zhu
Keyword(s):  
Privacy Preserving ◽  
Encrypted Data ◽  
Set Containment

Efficient location privacy-preserving range query scheme for vehicle sensing systems

2020 ◽  
Vol 106 ◽  
pp. 101714 ◽  
Author(s):  
Peng Hu ◽  
Yongli Wang ◽  
Quanbing Li ◽  
Yongjian Wang ◽  
Yanchao Li ◽  
...  
Keyword(s):  
Location Privacy ◽  
Range Query ◽  
Privacy Preserving ◽  
Sensing Systems

scholarly journals Privacy-Preserving Sorting Algorithms Based on Logistic Map for Clouds

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.

scholarly journals Secure and Low Energy Consumption Range Query in Tiered Sensor Networks

2016 ◽  
Vol 12 (07) ◽  
pp. 4 ◽  
Author(s):  
Song Ling ◽  
Qi Dong Yang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Value Chain ◽  
Range Query ◽  
Privacy Preserving ◽  
Low Energy ◽  
Wireless Sensor ◽  
Simulation Experiments ◽  
Low Energy Consumption

For the requirement of low energy consumption and high privacy-preserving in wireless sensor networks of range query, we propose a low energy consumption secure and verifiable range query protocol called SPRQ.SPRQ uses a novel prime aggregation to protect the privacy of the query data; We further propose an idea of the and value chain whereby data items collected by each sensor will be linked with each other just like a chain.The Sink verifies the integrity of query results by checking whether the data chain of each sensor is complete or not. The results of simulation experiments prove that prime aggregation can effectively reduce the amount of increased data in the prefix encoding process,so,network energy consumption is lower compared with other secure range query protocols.

Privacy-Preserving Tensor Decomposition Over Encrypted Data in a Federated Cloud Environment

2020 ◽  
Vol 17 (4) ◽  
pp. 857-868 ◽  
Author(s):  
Jun Feng ◽  
Laurence T. Yang ◽  
Qing Zhu ◽  
Kim-Kwang Raymond Choo
Keyword(s):  
Tensor Decomposition ◽  
Privacy Preserving ◽  
Cloud Environment ◽  
Encrypted Data
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