Nilay Khare

Big data privacy: a technological perspective and review

Big data is a term used for very large data sets that have more varied and complex structure. These characteristics usually correlate with additional difficulties in storing, analyzing and applying further procedures or extracting results. Big data analytics is the term used to describe the process of researching massive amounts of complex data in order to reveal hidden patterns or identify secret correlations. However, there is an obvious contradiction between the security and privacy of big data and the widespread use of big data. This paper focuses on privacy and security concerns in big data, differentiates between privacy and security and privacy requirements in big data. This paper covers uses of privacy by taking existing methods such as HybrEx, k-anonymity, T-closeness and L-diversity and its implementation in business. There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages (for example, data generation, data storage, and data processing) of a big data life cycle. The goal of this paper is to provide a major review of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms. This paper also presents recent techniques of privacy preserving in big data like hiding a needle in a haystack, identity based anonymization, differential privacy, privacy-preserving big data publishing and fast anonymization of big data streams. This paper refer privacy and security aspects healthcare in big data. Comparative study between various recent techniques of big data privacy is also done as well.

Hardware implementation of neural network with Sigmoidal activation functions using CORDIC

Activation function is the most important function in neural network processing. In this article, the field-programmable gate array (FPGA)-based hardware implementation of a multilayer feed-forward neural network, with a log sigmoid activation function and a tangent sigmoid (hyperbolic tangent) activation function has been presented, with more accuracy than any other previous implementation of a neural network with the same activation function. Accuracy is enhanced through the implementation of both the sigmoidal functions using COordinate Rotation DIgital Computer (CORDIC) algorithm. The CORDIC algorithm is a simple and effective method for calculation of the trigonometric and hyperbolic functions. Simulations and experiments have been performed on the ISim simulation engine of the Xilinx Framework, using the Very High Speed Integrated Circuit Hardware Description Language (VHDL) as the programming language. The results show accuracy for a 32-bit and 64-bit input/output, compromising with speed.

Bagasse Cogeneration in India: Status, Barriers

India is one of the largest consumers and producers of sugar in the world and is the worlds second largest producer next to Brazil of the sugarcane. Country has made impressive growth in the bagasse cogeneration. However, sustaining the growth is the real challenge. This article provides an overview of the current status, historical growth, technological status, policy, regulatory and fiscal support extended by the Govt of India as well as state governments to bagasse cogeneration. This work has identified the barriers in speedy adoption of the technology by sugar mills. The article concludes that support extended to bagasse cogeneration by the MNRE, especially to cooperative sugar mills, must be continued. The preferential tariff determined by CERC and SERCs for the bagasse cogeneration must take into account the increased support price offered to sugarcane by state governments as well as price offered to bagasse by competing technologies like paper and pulp industries. Strong RPO compliance by power distribution companies and streamlining of REC mechanism is required to attract industries, project developers and investors to invest in renewable energy in the country.

Implementation of graph algorithms over GPU: A comparative analysis

GPU (Graphics Processing Unit) provides high computational speed at a very low cost as compared to high end systems. The field of parallel processing using GPU is advancing very fast with a new technology being introduced in the field every day. With such advancements, it is necessary to review the major works done in this field. Graph traversal is one of the major challenges in this field. So far comprehensive review work covering major areas of the graph traversal techniques have not been reported much. This paper aims at surveying the implementation of different graph traversal techniques (viz. BFS, SSSP, APSP) using CUDA architecture and GPU.

String Matching Technique Based on Hardware: A Comparative Analysis

Network Intrusion Detection Systems is one of the most effective way of providing security to those connected to the network, and the string matching algorithm is the heart of the intrusion detection system. IDS checks both packet header and payload in order to detect content-based security threats.Payload scan requires efficient string matching techniques, since each incoming packet must be compared against the hundreds of known attacks. Checking every byte of every packet to see if it matches one of a set of ten thousand strings becomes a computationally intensive task as network speeds grows up. For high speed networks it can be difficult to keep up with intrusion detection using purely software approach without affecting performance of the system intended for designed application. It is essential to use hardware systems for intrusion detection. A string matching algorithm is implemented in hardware with the focus on increasing throughput, and reasonable area cost while maintaining the configurability provided by the software IDSs. This paper consist a review of different string matching techniques implemented in FPGA for detecting malicious packet over the network.

Improved k-Anonymity Privacy-Preserving Algorithm Using Madhya Pradesh State Election Commission Big Data

Modern technology produces a large number of public and private data sets that make the task of securing personal data unavoidable. Initially, priority was given to securing data for organizations and companies, but nowadays it is also necessary to provide security for personal data. Therefore, to achieve information security, the protection of individual data is critical. Data anonymization is a technique for preserving privacy in data publishing, which enables the publication of practically useful information for data mining while preserving the confidentiality of the information of the individual. This chapter presents the implementation of data anonymization using a proposed improved k-anonymity algorithm applied to a large candidate election data set acquired from the Madhya Pradesh (MP, India) State Election Commission. Along with greater privacy, the algorithm is executed in less time than the traditional k-anonymity algorithm, and as such, it is able to satisfy the data protection needs of the current big data environment.

Viable Crop Prediction Scenario in BigData Using a Novel Approach

Agriculture leads a vital role in the human surveillance where it lies as the initial step for the human civilization. Due to the excessive need for food, the agricultural practices are in large-scale production as a business which is termed as “Agribusiness”. Modern agriculture allows both biological and technological developments such as plant breeding, agrochemicals, genetic breeding, remote sensing, crop monitoring, sensor nodes, and automatic maintenance system. The integration of sensor nodes in the farming field leads to the generation of huge data which can need a learning algorithm for analyses to determine a specific solution. There are various machine learning algorithms in practices which are not suitable for handling large datasets. In this paper, a novel algorithm “Agrifi-prediction algorithm” is created which has the functionality of loading the dataset in hdfs and comparing the previous dataset with the current processing dataset. The experimental process is carried out by using the Hadoop framework with MapReduce programming model by analyzing the meteorological and soil dataset and finally compared with the machine learning algorithm to evaluate the accuracy.

Differential privacy: its technological prescriptive using big data

Data is being produced in large amounts and in rapid pace which is diverse in quality, hence, the term big data used. Now, big data has started to influence modern day life in almost every sphere, be it business, education or healthcare. Data being a part and parcel of everyday life, privacy has become a topic requiring emphasis. Privacy can be defined as the capacity of a person or group to seclude themselves or information about themselves, and thereby express them selectively. Privacy in big data can be achieved through various means but here the focus is on differential privacy. Differential privacy is one such field with one of the strongest mathematical guarantee and with a large scope of future development. Along these lines, in this paper, the fundamental ideas of sensitivity and privacy budget in differential privacy, the noise mechanisms utilized as a part of differential privacy, the composition properties, the ways through which it can be achieved and the developments in this field till date has been presented. The research gap and future directions have also been mentioned as part of this paper.

HCudaBLAST: an implementation of BLAST on Hadoop and Cuda

The world of DNA sequencing has not only been a difficult field since it was first worked upon, but it is also growing at an exponential rate. The amount of data involved in DNA searching is huge, thereby normal tools or algorithms are not suitable to handle this degree of data processing. BLAST is a tool given by National Center for Biotechnology Information (NCBI) to compare nucleotide or protein sequences to sequence databases and calculate the statistical significance of matches. Many variants of BLAST such as blastn, blastp, blastx, etc. are used to search for nucleotides, proteins, nucleotides-to-proteins sequences respectively. GPU-BLAST and HBLAST have already been proposed to handle the vast amount of data involved in searching DNA sequencing and they also speedup the searching process. In this article, we propose a new model for searching DNA sequences—HCudaBLAST. It involves CUDA processing and Hadoop combined for efficient searching. The results recorded after implementing HCudaBLAST are shown. This solution combines the multi-core parallelism of GPGPUs and the scalability feature provided by the Hadoop framework.

Transform for Simplified Weight Computations in the Fuzzy Analytic Hierarchy Process

A simplified procedure for weight computations from the pair-wise comparison matrices of triangular fuzzy numbers in the fuzzy analytic hierarchy process is proposed. A transform T:R3→R1 has been defined for mapping the triangular fuzzy numbers to equivalent crisp values. The crisp values have been used for eigenvector computations in a manner analogous to the computations of the original AHP method. The objective is to retain both the ability to capture and deal with inherent uncertainties of subjective judgments, which is the strength of fuzzy modeling and the simplicity, intuitive appeal, and power of conventional AHP which has made it a very popular decision making tool.