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

A Survey on Privacy Preserving Techniques in WSN

A great part of the existing tackle Wireless Sensor Networks (WSN) has concentrated on tending to the force and computational asset demands of WSN by the configuration of particular steering, MAC, and cross-layer conventions. As of late, there have been increased privacy concerns over the data gathered by and transmitted through WSN. The remote transmission needed by a WSN, and the self-composing nature of its structural engineering, makes privacy assurance for WSN a particularly testing issue. This paper gives a stateof-art review of privacy-preserving strategies for WSN. Specifically, we audit two fundamental classes of privacypreserving strategies for ensuring two sorts of private data, data-turned and connection situated privacy, separately. We likewise talk about a number of essential open challenges for future research. Our trust is that this paper sheds some light on a productive course of prospective research for privacy conservation in WSN