Privacy-Preserving ID3 Data Mining over Encrypted Data in Outsourced Environments with Multiple Keys

2017 ◽  
Author(s):  
Ye Li ◽  
Zoe L. Jiang ◽  
Xuan Wang ◽  
S.M. Yiu
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Encrypted Data ◽  
Multiple Keys

scholarly journals Privacy Preserving Mining in Code Profiling Data

2018 ◽  
Vol 8 (5) ◽  
Author(s):  
Meenakshi Kathayat
Keyword(s):  
Data Mining ◽  
Software Engineering ◽  
Future Development ◽  
Data Privacy ◽  
Software Metrics ◽  
Privacy Preserving ◽  
Sensitive Data ◽  
Privacy Preserving Data Mining ◽  
Encrypted Data

Privacy preserving data mining is an important issue nowadays for data mining. Since various organizations and people are generating sensitive data or information these days. They don’t want to share their sensitive data however that data can be useful for data mining purpose. So, due to privacy preserving mining that data can be mined usefully without harming the privacy of that data. Privacy can be preserved by applying encryption on database which is to be mined because now the data is secure due to encryption. Code profiling is a field in software engineering where we can apply data mining to discover some knowledge so that it will be useful in future development of software. In this work we have applied privacy preserving mining in code profiling data such as software metrics of various codes. Results of data mining on actual and encrypted data are compared for accuracy. We have also analyzed the results of privacy preserving mining in code profiling data and found interesting results.

scholarly journals Classification and Privacy Preserving Search of Multimedia Data

2018 ◽  
Vol 7 (3.34) ◽  
pp. 259
Author(s):  
J Jeejo Vetharaj ◽  
S Selvanayaki ◽  
M B.Suseela
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Classification Problem ◽  
Privacy Preserving ◽  
Multimedia Data ◽  
Relational Data ◽  
Encrypted Data ◽  
Classification Techniques ◽  
Security Models ◽  
Privacy Issues

Classification, which is commonly used task in data mining applications separates the data present in the database based on some category. For years and years, considering the rise of several privacy issues, solutions in the form of theoretical and practical have been proposed for the classification problem under various security models. However, for the late Notoriety about cloud computing, clients presently have the chance on outsource their data, clinched alongside encrypted form, and also those information mining assignments of the cloud.. The data on the cloud which is in encrypted form, therefore existing privacy preserving classification techniques are not applicable. In this paper, we focus on finding solution for the classification problem over the encrypted data .Users can store their data with encryption by the use of ordered relational data. So, the data is obtained correctly without decrypting. 

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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