Middleware for Preserving Privacy in Big Data

Abstract

With increased usage of IT solutions, a huge volume of data is generated from different sources like social networks, CRM, and healthcare applications, to name a few. The size of the data that is generated grows exponentially. As cloud computing provides an optimized, shared, and virtualized IT infrastructure, it is better to leverage the cloud services for storing and processing such Big Data. Securing the data is one of the major challenges in all the domains. Though security and privacy have been talked about for decades, there is still a growing need for high end methods for securing the rampant growth of data. The privacy of personal data, and to be more specific the health data, continues to be an important issue worldwide. Most of the health data in today’s IT world is being computerized. A patient’s health data may portray the different attributes such as his physical and mental health, its severity, financial status, and much more. Moreover, the medical data that are collected from the patients are being shared with other stakeholders of interest like doctors, insurance companies, pharmacies, researchers, and other health care providers. Individuals raise concern about the privacy of their health data in such a shared environment.

Health data analytics using scalable logistic regression with stochastic gradient descent

“…However in personal healthcare system where sensors are installed and is produced data continuously, the amount of data to be stored and processed becomes a significant problem in real life. Relational database management system (RDBMS) is generally used to store the traditional data, but day by day the volume, velocity and variety of sensor data is growing towards Exabyte (Thilagavathi et al, 2014;Victor et al, 2016). This requires advanced tools and techniques to store, process and display such large amount of sensor data to the end users.…”

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

“…The basic characteristics of big data can be defined by 3Vs: the large volume of data, the wide variety of types of data and the velocity the data in and out. Much of the data acquired today is not in the structured format; hence heterogeneity, complexity, scale, timeliness and privacy with big data pose challenges in every stage of processing (Thilagavathi et al, 2014;Victor et al, 2016). It is not possible to process big data by traditional data processing frameworks; it requires new techniques and architectures to extract useful information from large amount of data.…”