A cost-effective 2-tier security paradigm to safeguard cloud data with faster authentication

The recent technological advancement has taken cloud computing (CC) infrastructure to a significant level where the increasing level of research interest laid upon cost-effective storage management.  Owing to the potential distributed and pervasive storage facility, it lacks efficiency towards fully preserving the integrity of user data attributes. Thereby, the collaborative sharing of user data leads to a situation which opens up various forms of security loop-holes. In the past various forms of security protocols are witnessed which have attempted to solve this similar issue with cryptographic solutions but at the same time lacks sustainability and robustness from a computational perspective. Thereby, the study introduces a 2-tier framework which offers higher-degree of access control along with Virtual Machine (VM) storage security. The study basically optimizes the performance of the model by speeding up the authentication process. The performance validation of the system has been done with respect to conventional encryption standards. The outcome obtained demonstrate that the proposed solution outperforms the existing security standards in terms of processing time, time to generate a secret key and key size for encryption

Cloud Based Framework for Autism Spectrum Disorder Therapy App

In the current era of connected devices like smart phones, the demand for data storage is increasing drastically for some set of applications involving multiuser. We require a centralized storage system where data can be accessed from any part of the world using various devices like mobiles and tabs. The cloud provides services for storing data on remote servers which can be accessed through the Internet. It is maintained, operated and managed by a cloud storage service provider on storage servers that are built on virtualization techniques and has large computational power compared to the mobile devices. The paper presented here proposes a cloud based framework for the application “AshaDeep” which was developed to provide technological support for autistic children. This mobile application generates huge number of images and data in a multiuser environment as a part of learning and evaluation activity. In this app we aim to unite multiple users by developing a common platform to track the progress of the autism children and combat autism

DRBM-ClustNet: A Deep Restricted Boltzmann-Kohonen Architecture for Data Clustering

A Bayesian Deep Restricted Boltzmann-Kohonen architecture for data clustering termed as DRBM-ClustNet is proposed. This core-clustering engine consists of a Deep Restricted Boltzmann Machine (DRBM) for processing unlabeled data by creating new features that are uncorrelated and have large variance with each other. Next, the number of clusters are predicted using the Bayesian Information Criterion (BIC), followed by a Kohonen Network-based clustering layer. The processing of unlabeled data is done in three stages for efficient clustering of the non-linearly separable datasets. In the first stage, DRBM performs non-linear feature extraction by capturing the highly complex data representation by projecting the feature vectors of $d$ dimensions into $n$ dimensions. Most clustering algorithms require the number of clusters to be decided a priori, hence here to automate the number of clusters in the second stage we use BIC. In the third stage, the number of clusters derived from BIC forms the input for the Kohonen network, which performs clustering of the feature-extracted data obtained from the DRBM. This method overcomes the general disadvantages of clustering algorithms like the prior specification of the number of clusters, convergence to local optima and poor clustering accuracy on non-linear datasets. In this research we use two synthetic datasets, fifteen benchmark datasets from the UCI Machine Learning repository, and four image datasets to analyze the DRBM-ClustNet. The proposed framework is evaluated based on clustering accuracy and ranked against other state-of-the-art clustering methods. The obtained results demonstrate that the DRBM-ClustNet outperforms state-of-the-art clustering algorithms.Comment: 14 pages, 7 figure

Generic CBTS: Correlation based Transformation Strategy for Privacy Preserving Data Mining

Mining useful knowledge from corpus of data has become an important application in many fields. Data Mining algorithms like Clustering, Classification work on this data and provide crisp information for analysis. As these data are available through various channels into public domain, privacy for the owners of the data is increasing need. Though privacy can be provided by hiding sensitive data, it will affect the Data Mining algorithms in knowledge extraction, so an effective mechanism is required to provide privacy to the data and at the same time without affecting the Data Mining results. Privacy concern is a primary hindrance for quality data analysis. Data mining algorithms on the contrary focus on the mathematical nature than on the private nature of the information. Therefore instead of removing or encrypting sensitive data, we propose transformation strategies that retain the statistical, semantic and heuristic nature of the data while masking the sensitive information. The proposed Correlation Based Transformation Strategy (CBTS) combines Correlation Analysis in tandem with data transformation techniques such as Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Non Negative Matrix Factorization (NNMF) provides the intended level of privacy preservation and enables data analysis. The proposed technique will work for numerical, ordinal and nominal data. The outcome of CBTS is evaluated on standard datasets against popular data mining techniques with significant success and Information Entropy is also accounted

A brief survey on privacy preserving data mining techniques

With the onset of the digital revolution, organizations are increasingly maintaining a huge amount of information on their databases and use data mining tools to extract useful information for their business intelligence. The problem with the availability of the digital information is the scarce privacy leakage. In many business domains, leakage of personal information of the client either directly or through data mining tools can lead to loss of competitive edge of the company, loss of revenue and customer churn. Companies are pushing for encryption and other data transformation methods to keep the data private. But mining tools which invoke algorithms like clustering, classification etc. may not work properly on the transformed data. In this paper, we analyze the privacy preserving data mining solutions and privacy leakage in them through indirect means. The main objective of this paper is to identify the open areas of

BELMKN : Bayesian extreme learning machines Kohonen Network

This paper proposes the Bayesian Extreme Learning Machine Kohonen Network (BELMKN) framework to solve the clustering problem. The BELMKN framework uses three levels in processing nonlinearly separable datasets to obtain efficient clustering in terms of accuracy. In the first level, the Extreme Learning Machine (ELM)-based feature learning approach captures the nonlinearity in the data distribution by mapping it onto a d-dimensional space. In the second level, ELM-based feature extracted data is used as an input for Bayesian Information Criterion (BIC) to predict the number of clusters termed as a cluster prediction. In the final level, feature-extracted data along with the cluster prediction is passed to the Kohonen Network to obtain improved clustering accuracy. The main advantage of the proposed method is to overcome the problem of having a priori identifiers or class labels for the data; it is difficult to obtain labels in most of the cases for the real world datasets. The BELMKN framework is applied to 3 synthetic datasets and 10 benchmark datasets from the UCI machine learning repository and compared with the state-of-the-art clustering methods. The experimental results show that the proposed BELMKN-based clustering outperforms other clustering algorithms for the majority of the datasets. Hence, the BELMKN framework can be used to improve the clustering accuracy of the nonlinearly separable datasets.Published versio

Multisource keyword extraction and graph construction for privacy preservation

Privacy preservation is an important branch of Data Mining which handles hiding of an individual's sensitive data without affecting the data usability. This paper proposes a new technique to provide privacy preservation of sensitive data based on the semantic context. Multisource Keyword Extraction and Graph Construction for Privacy Preservation involves extracting keywords from various data formats and preserving privacy among the keywords extracted using the techniques of Vector Marking. Initially, data cleaning and preprocessing is done on the document to extract keywords by applying techniques such as parsing, duplicate elimination, stemming and indexing. The document can be either PDF, SQL or Word files. After preprocessing, a context graph is generated from the keywords extracted with the help of context dictionaries such as WordNet and DBpedia. This context graph acts as a primary source of reference for all user queries. Privacy preservation of sensitive information is achieved using various Vector Marking techniques. The data input by the user can be classified as structured, unstructured and semi-structured data. Appropriate Vector Marking approaches are used for the given input data format. The keyword specified by the user in the input data as private is queried in the context graph to obtain the correlated words and these words are hidden from the access of the other users. Thus solving some of the issues related to privacy leakage. © 2017 ACM

SWCTE: Semantic weighted context tagging engine for privacy preserving data mining

Privacy Preserving in Data Mining is a very important area which deals with hiding an individual's sensitive data without affecting the usability of data. In this paper, we put forward a technique to provide privacy preservation of sensitive data based on the semantic context. Our approach encapsulates various techniques of Text-processing, keyphrase extraction, Cooccurrence analysis, ontology construction and query analysis. To handle privacy issues Correlation Based Transformation Strategy (CBTS) is performed on sensitive data, additionally we can add custom properties to the attributes of the ontology to indicate the sensitive data. Our experimental results indicate that our solution is effective in marking the private data using the semantic context of the input text. The main goal of our work is to construct a module which acts as an intermediate step in pre-processing for data mining while preserving the privacy. © 2016 IEEE