Privacy Preservation in Analyzing E-Health Records in Big Data Environment

Increased use of the Internet and progress in Cloud computing creates a large new datasets with increasing value to business. Data need to be processed by cloud applications are emerging much faster than the computing power. Hadoop-MapReduce has become powerful computation model to address these problems. Nowadays many cloud services require users to share their confidential data like electronic health records for research analysis or data mining, which brings privacy concerns. K-anonymity is one of the widely used privacy model. The scale of data in cloud applications rises extremely in agreement with the Big Data tendency, thereby creating it a dispute for conventional software tools to process such large scale data within an endurable lapsed time. As a consequence, it is a dispute for current anonymization techniques to preserve privacy on confidential extensible data sets due to their inadequacy of scalability. In this project, we propose an extensible two-phase approach to anonymize scalable data sets using dynamic MapReduce framework, Top Down Specialization (TDS) Algorithm and k-Anonymity privacy model. The resources are optimized via three key aspects. First, the under-utilization of map and reduce tasks is improved based on Dynamic Hadoop Slot Allocation (DHSA). Second, the performance tradeoff between the single job and a batch of jobs is balanced using the Speculative Execution Performance Balancing (SEPB). Third, data locality can be improved without any impact on fairness using Slot Pre Scheduling. Experimental evaluation results demonstrate that with this project, the scalability, efficiency and privacy of data sets can be significantly improved over existing approaches. DOI: 10.17762/ijritcc2321-8169.160413

Hybrid clouds for data-Intensive, 5G-Enabled IoT applications: an overview, key issues and relevant architecture

Hybrid cloud multi-access edge computing (MEC) deployments have been proposed as efficient means to support Internet of Things (IoT) applications, relying on a plethora of nodes and data. In this paper, an overview on the area of hybrid clouds considering relevant research areas is given, providing technologies and mechanisms for the formation of such MEC deployments, as well as emphasizing several key issues that should be tackled by novel approaches, especially under the 5G paradigm. Furthermore, a decentralized hybrid cloud MEC architecture, resulting in a Platform-as-a-Service (PaaS) is proposed and its main building blocks and layers are thoroughly described. Aiming to offer a broad perspective on the business potential of such a platform, the stakeholder ecosystem is also analyzed. Finally, two use cases in the context of smart cities and mobile health are presented, aimed at showing how the proposed PaaS enables the development of respective IoT applications.Peer ReviewedPostprint (published version

Data Anonymization Using Map Reduce on Cloud based A Scalable Two-Phase Top-Down Specialization

A large number of cloud services require users to impart` private data like electronic health records for data analysis or Mining, bringing privacy concerns. Anonymizing information sets through generalization to fulfill certain security prerequisites, for example, k-anonymity is a broadly utilized classification of protection safeguarding procedures At present, the scale of information in numerous cloud applications increments immensely as per the Big Data pattern, in this manner making it a test for normally utilized programming instruments to catch, oversee, and process such substantial scale information inside a bearable slipped by time. As an issue, it is a test for existing anonymization methodologies to accomplish security protection on security touchy extensive scale information sets because of their inadequacy of adaptability. In this paper, we propose a versatile two-stage top-down specialization (TDS) methodology to anonymize huge scale information sets utilizing the Map reduce schema on cloud. Experimental evaluation results demonstrate that with our approach, the scalability and efficiency of TDS can be significantly improved over existing approaches

Data Anonymization for Privacy Preservation in Big Data

Cloud computing provides capable ascendable IT edifice to provision numerous processing of a various big data applications in sectors such as healthcare and business. Mainly electronic health records data sets and in such applications generally contain privacy-sensitive data. The most popular technique for data privacy preservation is anonymizing the data through generalization. Proposal is to examine the issue against proximity privacy breaches for big data anonymization and try to recognize a scalable solution to this issue. Scalable clustering approach with two phase consisting of clustering algorithm and K-Anonymity scheme with Generalisation and suppression is intended to work on this problem. Design of the algorithms is done with MapReduce to increase high scalability by carrying out dataparallel execution in cloud. Wide-ranging researches on actual data sets substantiate that the method deliberately advances the competence of defensive proximity privacy breaks, the scalability and the efficiency of anonymization over existing methods. Anonymizing data sets through generalization to gratify some of the privacy attributes like k- Anonymity is a popularly-used type of privacy preserving methods. Currently, the gauge of data in numerous cloud surges extremely in agreement with the Big Data, making it a dare for frequently used tools to actually get, manage, and process large-scale data for a particular accepted time scale. Hence, it is a trial for prevailing anonymization approaches to attain privacy conservation for big data private information due to scalabilty issues