Security aware information classification in health care big data

These days e-medical services frameworks are getting famous for taking care of patients from far-off spots, so a lot of medical services information like the patient’s name, area, contact number, states of being are gathered distantly to treat the patients. A lot of information gathered from the different assets is named big data. The enormous sensitive information about the patient contains delicate data like systolic BP, pulse, temperature, the current state of being, and contact number of patients that should be recognized and sorted appropriately to shield it from abuse. This article presents a weightbased similarity (WBS) strategy to characterize the enormous information of health care data into two classifications like sensitive information and normal information. In the proposed method, the training dataset is utilized to sort information and it comprises of three fundamental advances like information extraction, mapping of information with the assistance of the training dataset, evaluation of the weight of input data with the threshold value to classify the data. The proposed strategy produces better outcomes with various assessment boundaries like precision, recall, F1 score, and accuracy value 92% to categorize the big data. Weka tool is utilized for examination among WBS and different existing order procedures

Adaptive PVD Steganography Using Horizontal, Vertical, and Diagonal Edges in Six-Pixel Blocks

The traditional pixel value differencing (PVD) steganographical schemes are easily detected by pixel difference histogram (PDH) analysis. This problem could be addressed by adding two tricks: (i) utilizing horizontal, vertical, and diagonal edges and (ii) using adaptive quantization ranges. This paper presents an adaptive PVD technique using 6-pixel blocks. There are two variants. The proposed adaptive PVD for 2×3-pixel blocks is known as variant 1, and the proposed adaptive PVD for 3×2-pixel blocks is known as variant 2. For every block in variant 1, the four corner pixels are used to hide data bits using the middle column pixels for detecting the horizontal and diagonal edges. Similarly, for every block in variant 2, the four corner pixels are used to hide data bits using the middle row pixels for detecting the vertical and diagonal edges. The quantization ranges are adaptive and are calculated using the correlation of the two middle column/row pixels with the four corner pixels. The technique performs better as compared to the existing adaptive PVD techniques by possessing higher hiding capacity and lesser distortion. Furthermore, it has been proven that the PDH steganalysis and RS steganalysis cannot detect this proposed technique

Optimization of Energy Aware Path Routing Protocol in Wireless Sensor Networks

Strength conservation is one of the biggest challenges to the successful WSNs since the tiny  very limited resource nodes  such as energy, memory space| as well as communication and computation capabilities. the sensors are unattended Implemented  and battery recharge is almost impossible. So  many investigations have be done in redirecting energy efficient algorithms or protocols for WSNs. Our reasons behinds the study of number is based on the following three aspects. Initially of all First, we see That immediate transmittal is employed under small scale while multi-hop network transmittal network is employed under mass. All of us want to find the Which factors influence the transmittal manner. Second, it is Commonly That multi-hop agree transmitting more energy efficient than Usually transmitting When the average solitary source to destination distance is large. Yet ,}how to look for the optimal hop number in order That the overall energy consumption is  nominal is not well  tackled. Third, the hot location phenomenon the networking lifetime influences directly. After that all of us recommend to Optimization of energy aware routing path (OEAPR) algorithm, Which incorporate the overall routing mechanism With hop-based direction-finding nature During process in WSNs

High Capacity Image Steganography Using Modified LSB Substitution and PVD against Pixel Difference Histogram Analysis

The combination of pixel value differencing (PVD) and least significant bit (LSB) substitution gives higher capacity and lesser distortion. However, there are three issues to be taken into account: (i) fall off boundary problem (FOBP), (ii) pixel difference histogram (PDH) analysis, and (iii) RS analysis. This paper proposes a steganography technique in two variants using combination of modified LSB substitution and PVD by taking care of these three issues. The first variant operates on 2 × 3 pixel blocks and the second technique operates on 3 × 3 pixel blocks. In one of the pixels of a block, embedding is performed using modified LSB substitution. Based on the new value of this pixel, difference values with other neighboring pixels are calculated. Using these differences, PVD approach is applied. The edges in multiple directions are exploited, so PDH analysis cannot detect this steganography. The LSB substitution is performed in only one pixel of the block, so RS analysis also cannot detect this steganography. To address the FOBP, suitable equations are used during embedding procedure. The experimental results such as bit rate and distortion measure are satisfactory

Digital Image Steganography Using Eight-Directional PVD against RS Analysis and PDH Analysis

The least significant bit (LSB) substitution techniques are detected by RS analysis and the traditional pixel value differencing (PVD) approaches are detected by pixel difference histogram (PDH) analysis. The PVD steganography can escape from PDH analysis by using the edges in multiple directions. This paper proposes a steganography technique by exploiting the edges in eight directions and also using LSB substitution to resist from both RS analysis and PDH analysis. For every 3×3 pixel block the central pixel is embedded with 3 or 4 bits of data by modified LSB substitution technique. Then this new value of the central pixel is utilized to calculate eight difference values with eight neighboring pixels. These eight difference values are used to hide the data. There are two types with regard to two different range tables. Type 1 uses 3 bit modified LSB substitution and range table 1. Type 2 uses 4 bit modified LSB substitution and range table 2. Type 1 and type 2 are also known as variant 1 and variant 2, respectively. Type 1 possesses higher PSNR and type 2 possesses higher hiding capacity

Digital Image Steganography Using LSB Substitution, PVD, and EMD

To protect from pixel difference histogram (PDH) analysis and RS analysis, two hybrid image steganography techniques by appropriate combination of LSB substitution, pixel value differencing (PVD), and exploiting modification directions (EMD) have been proposed in this paper. The cover image is traversed in raster scan order and partitioned into blocks. The first technique operates on 2 × 2 pixel blocks and the second technique operates on 3 × 3 pixel blocks. For each block, the average pixel value difference, d, is calculated. If d value is greater than 15, the block is in an edge area, so a combination of LSB substitution and PVD is applied. If d value is less than or equal to 15, the block is in a smooth area, so a combination of LSB substitution and EMD is applied. Each of these two techniques exists in two variants (Type 1 and Type 2) with respect to two different range tables. The hiding capacities and PSNR of both the techniques are found to be improved. The results from experiments prove that PDH analysis and RS analysis cannot detect these proposed techniques