Clustering Based Efficient Privacy Preserving Multi Keyword Search Over Encrypted Data

2018 ◽  
Author(s):  
Neha Mahajan ◽  
Vaishali Barkade
Keyword(s):  
Keyword Search ◽  
Privacy Preserving ◽  
Encrypted Data

scholarly journals Privacy-Preserving and Efficient Public Key Encryption with Keyword Search Based on CP-ABE in Cloud

Cryptography ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 28
Author(s):  
Yunhong Zhou ◽  
Shihui Zheng ◽  
Licheng Wang
Keyword(s):  
Access Control ◽  
Data Privacy ◽  
Keyword Search ◽  
Good Method ◽  
Privacy Preserving ◽  
Public Key ◽  
Public Key Encryption ◽  
Encrypted Data ◽  
Fine Grained ◽  
Attribute Based Encryption

In the area of searchable encryption, public key encryption with keyword search (PEKS) has been a critically important and promising technique which provides secure search over encrypted data in cloud computing. PEKS can protect user data privacy without affecting the usage of the data stored in the untrusted cloud server environment. However, most of the existing PEKS schemes concentrate on data users’ rich search functionalities, regardless of their search permission. Attribute-based encryption technology is a good method to solve the security issues, which provides fine-grained access control to the encrypted data. In this paper, we propose a privacy-preserving and efficient public key encryption with keyword search scheme by using the ciphertext-policy attribute-based encryption (CP-ABE) technique to support both fine-grained access control and keyword search over encrypted data simultaneously. We formalize the security definition, and prove that our scheme achieves selective indistinguishability security against an adaptive chosen keyword attack. Finally, we present the performance analysis in terms of theoretical analysis and experimental analysis, and demonstrate the efficiency of our scheme.

Privacy-preserving conjunctive keyword search on encrypted data with enhanced fine-grained access control

2019 ◽  
Vol 23 (2) ◽  
pp. 959-989 ◽  
Author(s):  
Qiang Cao ◽  
Yanping Li ◽  
Zhenqiang Wu ◽  
Yinbin Miao ◽  
Jianqing Liu
Keyword(s):  
Access Control ◽  
Keyword Search ◽  
Privacy Preserving ◽  
Encrypted Data ◽  
Fine Grained

Privacy-Preserving Keyword Search Over Encrypted Data in Cloud Computing

2013 ◽  
pp. 189-212 ◽  
Author(s):  
Wenhai Sun ◽  
Wenjing Lou ◽  
Y. Thomas Hou ◽  
Hui Li
Keyword(s):  
Cloud Computing ◽  
Keyword Search ◽  
Privacy Preserving ◽  
Encrypted Data

scholarly journals Privacy Preserving Data Mining with Multi Keyword Ranked Search for Medical Data

2020 ◽  
Vol 9 (3) ◽  
pp. 21-25
Keyword(s):  
Data Mining ◽  
Efficient Method ◽  
Privacy Preservation ◽  
Keyword Search ◽  
Privacy Preserving ◽  
Medical Data ◽  
Privacy Preserving Data Mining ◽  
Encrypted Data ◽  
Outsourced Data ◽  
Ranked Search

Privacy Preserving Data Mining (PPDM) maintains the privacy of data stored in cloud. This work aims to protect outsourced data in cloud, and also permit multi keyword search over the encrypted data in a secure way by NLP process without downloading and decrypting all files. Different methods for privacy preservation were analyzed and randomization for multilevel trust is proposed along with an efficient method for keyword search in cloud.

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

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
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