A Study on Big Data Privacy Protection Models using Data Masking Methods

In today’s predictive analytics world, data engineering play a vital role, data acquisition is carried out from various source systems and process as per the business applications and domain. Big Data integrates, governs, and secures big data with repeatable, reliable, and maintainable processes. Through volume, speed, and assortment of information characteristics try to reveal business esteem from enormous information. However, with information that is frequently deficient, conflicting, ungoverned, and unprotected, which is hazardous and enormous information being a risk instead of an advantage. What's more, with conventional methodologies that are manual and unpredictable, huge information ventures take too long to acknowledge business esteem. Reasonably and over and again conveying business esteem from enormous information requires another technique. In this connection, raw data has to be moved between onsite and offshore environment during this course of action, data privacy is a major concern and challenge. A Big Data Privacy platform can make it easier to detect, investigate, assess, and remediate threats from intruders. We tried to do complete study of Big Data Privacy using data masking methods on various data loads and different types. This work will help data quality analyst and big data developers while building the big data applications

Big data based security analytics for protecting virtualized infrastructures in cloud computing

Virtualized infrastructure in cloud computing has become an attractive target for cyberattackers to launch advanced attacks. This paper proposes a novel big data based security analytics approach to detecting advanced attacks in virtualized infrastructures. Network logs as well as user application logs collected periodically from the guest virtual machines (VMs) are stored in the Hadoop Distributed File System (HDFS). Then, extraction of attack features is performed through graph-based event correlation and MapReduce parser based identification of potential attack paths. Next, determination of attack presence is performed through two-step machine learning, namely logistic regression is applied to calculate attack's conditional probabilities with respect to the attributes, and belief propagation is applied to calculate the belief in existence of an attack based on them. Experiments are conducted to evaluate the proposed approach using well-known malware as well as in comparison with existing security techniques for virtualized infrastructure. The results show that our proposed approach is effective in detecting attacks with minimal performance overhead