Integer factorization is one of the vital algorithms discussed as a part of analysis of any black-box cipher suites where the cipher algorithm is based on number theory. The origin of the problem is from Discrete Logarithmic Problem which appears under the analysis of the cryptographic algorithms as seen by a cryptanalyst. The integer factorization algorithm poses a potential in computational science too, obtaining the factors of a very large number is challenging with a limited computing infrastructure. This paper analyses the Pollard’s Rho heuristic with a varying input size to evaluate the performance under a multi-core environment and also to estimate the threshold for each computing infrastructure

Our life nowadays relies much on technologies and online services net banking, e-voting and so on. So, there is a necessity to secure the data that is transmitted through the internet. However, while performing decryption, it sometimes led to privacy violation so there is need to operate on users encrypted data without knowing the original plaintext.This paper represents the implementation of two-layer cryptosystem using paillier and elgamal algorithm both following asymmetric encryption. It is mainly focusing the challenges of privacy protection and secure utilization of information, where homomorphy encryption is gaining attention. Additive homomorphism is used in paillier cryptosystem which is used in fields like secure biometrics and electronic voting. Elgamal ensures that paillier encrypted data is secured that ensures two-layer encryption.

Hadoop finds its area in Big Data Analytics for analysing huge amounts of data. Hadoop implements MapReduce to distribute data to different clusters. Data compression is adopted in order to reduce the memory space occupied by the data. The concept of MapReduce performance with Data Compression focuses on a number of compression codecs of Hadoop cluster such as snappy, gzip, Iz4, bzip2 and deflate. The Big data analytics in health care faces good benefits and also with all its associated components focuses with the proposal of a big data health care architecture. Big data analytics is an emerging field for extraction of closely connected information from very huge data-sets and focuses on the improvement of decision making with improved decision making. The educational system and academic trends of students needs to map up with the current trends in technological advancement which accumulates large amount of data which is unstructured and needs to be analysed. Data mining tools are required to obtain information with meaning by converting unstructured data to structured data. Data has become necessary part of every individual, industry, economy, business function and organization. As this data set increases, selecting the relevant information becomes a tedious task. The on-command and ondemand nature of digital universe gives creation of a data category called the Big Data because of its sheer volume, variety and velocity. It proposes computational and analytical challenges which includes measurement errors, scalability and storage bottleneck and noise accumulation.

Security is a major concern for wireless multimedia networks because of their role in providing various services. Traditional security techniques have inadequacies in identifying emerging security threats and also lacks in computing efficiency. Furthermore, conventional upper-layer authentication doesn't provide any protection for physical layer, thus leading to leakage of privacy data. Keep these issues in mind, the paper has envisioned an artificial intelligence-based security authentication system that is lightweight, adaptive and doesn't require any explicit programming. The neural network is built on convolutional filters which explore the data and learns the features or characteristic of the data. With this learned feature, the model will be able to recognize whether a wireless multimedia device present in a network is legitimate or not. Experimental analysis and validation have been performed on the trained model and ensure that the authentication of wireless multimedia devices can be achieved and also ensuring lightweight authentication system, which ensures less computation needs. The different neural model is also trained using gaussian noise of different standard deviation so that it can be used in a practical scenario like smart industry etc.

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Hadoop has become an important tool for the researchers and scientists in order to store and analyze huge amount of data. This huge data is placed in Hadoop with the help of Hadoop Distributed File System (HDFS). Block placement policy is employed in HDFS to split a really huge file into blocks and place these block across the cluster in an exceedingly distributed manner. Basically, Hadoop and HDFS are designed to works expeditiously on the consistent cluster. However during this era of networking, we cannot think about having solely a cluster of consistent nodes. So, there's the necessity of storage policy which will work expeditiously on each consistent still because of the heterogeneous cluster. Thus, the need of applications which will be executing in a time-efficiently manner and supporting consistent still because the heterogeneous setting will be sufficient. In Hadoop data1. MapReduce is programming framework for writing Map-Reduce applications which enables them to run on the distributed platform in parallel. MapReduce permits the applications to run on Hadoop environment.Hadoop uses HDFS block placement policy to place the data blocks on nodes. Hadoop cluster gets unbalanced every now and then, because of overutilization of few nodes against the less used nodes or recently created other new nodes with no blocks hold on them. To resolve this case, Hadoop encompasses an inherent tool known as HDFS Balancer.