scholarly journals OLPSO_Iot:A Privacy Preservation Encrypted Data for Internet of Things (Iot) Data Security in Cloud Computation Environment

2020 ◽  
Vol 9 (3) ◽  
pp. 3636-3641
Keyword(s):  
Internet Of Things ◽  
Cloud Storage ◽  
Privacy Preservation ◽  
Data Transfer ◽  
Cuckoo Search ◽  
Original Data ◽  
Data Sets ◽  
Data Intensive ◽  
Intermediate Data ◽  
Cloud Computation

The Internet of Things connected devices will send data to cloud storage but cloud storage management carries their applications without any infrastructure investment by distributed computing. Therefore, manyindustries are doing their business in the cloud. For a while,the processing ofthe original data setand several intermediate data sets wasrendered by data-intensive applications. However, a challenging task is to support the privacy of the intermediate data set.Inourearlier research, optimal privacy preserving based data search in the cloud was presented using cuckoo search encryption algorithm toimprove the security.In this paper applied the orthogonal learning PSO(OLPSO) algorithm tohelp secure the IoT data in a cloud environment and improve the data transfer as well as decrease the data loss rate with efficient memory

scholarly journals Opposition Intensity-Based Cuckoo Search Algorithm for Data Privacy Preservation

2019 ◽  
Vol 29 (1) ◽  
pp. 1441-1452 ◽  
Author(s):  
G.K. Shailaja ◽  
C.V. Guru Rao
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Original Data ◽  
Cuckoo Search Algorithm ◽  
Research Issues ◽  
Novel Approach ◽  
Two Phases

Abstract Privacy-preserving data mining (PPDM) is a novel approach that has emerged in the market to take care of privacy issues. The intention of PPDM is to build up data-mining techniques without raising the risk of mishandling of the data exploited to generate those schemes. The conventional works include numerous techniques, most of which employ some form of transformation on the original data to guarantee privacy preservation. However, these schemes are quite multifaceted and memory intensive, thus leading to restricted exploitation of these methods. Hence, this paper intends to develop a novel PPDM technique, which involves two phases, namely, data sanitization and data restoration. Initially, the association rules are extracted from the database before proceeding with the two phases. In both the sanitization and restoration processes, key extraction plays a major role, which is selected optimally using Opposition Intensity-based Cuckoo Search Algorithm, which is the modified format of Cuckoo Search Algorithm. Here, four research issues, such as hiding failure rate, information preservation rate, and false rule generation, and degree of modification are minimized using the adopted sanitization and restoration processes.

A New User-controlled and Efficient Encrypted Data Sharing Model in Cloud Storage

2019 ◽  
Vol 13 (4) ◽  
pp. 356-363
Author(s):  
Yuezhong Wu ◽  
Wei Chen ◽  
Shuhong Chen ◽  
Guojun Wang ◽  
Changyun Li
Keyword(s):  
Data Sharing ◽  
Data Security ◽  
Cloud Storage ◽  
Screening Program ◽  
Original Data ◽  
Third Party ◽  
Active System ◽  
Encrypted Data ◽  
Active User ◽  
User Data

Background: Cloud storage is generally used to provide on-demand services with sufficient scalability in an efficient network environment, and various encryption algorithms are typically applied to protect the data in the cloud. However, it is non-trivial to obtain the original data after encryption and efficient methods are needed to access the original data. Methods: In this paper, we propose a new user-controlled and efficient encrypted data sharing model in cloud storage. It preprocesses user data to ensure the confidentiality and integrity based on triple encryption scheme of CP-ABE ciphertext access control mechanism and integrity verification. Moreover, it adopts secondary screening program to achieve efficient ciphertext retrieval by using distributed Lucene technology and fine-grained decision tree. In this way, when a trustworthy third party is introduced, the security and reliability of data sharing can be guaranteed. To provide data security and efficient retrieval, we also combine active user with active system. Results: Experimental results show that the proposed model can ensure data security in cloud storage services platform as well as enhance the operational performance of data sharing. Conclusion: The proposed security sharing mechanism works well in an actual cloud storage environment.

scholarly journals Self-Organising Map Approach to Individual Profiles: Age, Sex and Culture in Internet Dating

2006 ◽  
Vol 11 (1) ◽  
pp. 114-129 ◽  
Author(s):  
Teemu Suna ◽  
Michael Hardey ◽  
Jouni Huhtinen ◽  
Yrjö Hiltunen ◽  
Kimmo Kaski ◽  
...  
Keyword(s):  
Personal Information ◽  
Analytical Techniques ◽  
Original Data ◽  
Data Sets ◽  
Complex Data ◽  
Internet Dating ◽  
Visual Maps ◽  
Unsupervised Neural Network ◽  
Recent Developments ◽  
Self Organising Map

A marked feature of recent developments in the networked society has been the growth in the number of people making use of Internet dating services. These services involve the accumulation of large amounts of personal information which individuals utilise to find others and potentially arrange offline meetings. The consequent data represent a challenge to conventional analysis, for example, the service that provided the data used in this paper had approximately 5,000 users all of whom completed an extensive questionnaire resulting in some 300 parameters. This creates an opportunity to apply innovative analytical techniques that may provide new sociological insights into complex data. In this paper we utilise the self-organising map (SOM), an unsupervised neural network methodology, to explore Internet dating data. The resulting visual maps are used to demonstrate the ability of SOMs to reveal interrelated parameters. The SOM process led to the emergence of correlations that were obscured in the original data and pointed to the role of what we call ‘cultural age’ in the profiles and partnership preferences of the individuals. Our results suggest that the SOM approach offers a well established methodology that can be easily applied to complex sociological data sets. The SOM outcomes are discussed in relation to other research about identifying others and forming relationships in a network society.

Bayesian Classifier for Sparsity-Promoting Feature Selection

2015 ◽  
Vol 29 (06) ◽  
pp. 1550022 ◽  
Author(s):  
Danlei Xu ◽  
Lan Du ◽  
Hongwei Liu ◽  
Penghui Wang
Keyword(s):  
Feature Selection ◽  
Synthetic Data ◽  
Original Data ◽  
Radar Data ◽  
Bayesian Classifier ◽  
Classification Model ◽  
Data Sets ◽  
Data Set ◽  
Classification Boundary ◽  
Nonlinear Mappings

A Bayesian classifier for sparsity-promoting feature selection is developed in this paper, where a set of nonlinear mappings for the original data is performed as a pre-processing step. The linear classification model with such mappings from the original input space to a nonlinear transformation space can not only construct the nonlinear classification boundary, but also realize the feature selection for the original data. A zero-mean Gaussian prior with Gamma precision and a finite approximation of Beta process prior are used to promote sparsity in the utilization of features and nonlinear mappings in our model, respectively. We derive the Variational Bayesian (VB) inference algorithm for the proposed linear classifier. Experimental results based on the synthetic data set, measured radar data set, high-dimensional gene expression data set, and several benchmark data sets demonstrate the aggressive and robust feature selection capability and comparable classification accuracy of our method comparing with some other existing classifiers.

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