A Survey on Big Data Security Algorithms

Author Name: K. Lakshmi Prasanna, M. Thrilok Reddy, S. Shiva Prakash
Author Email: prasannaengg321@gmail.com

Abstract

Technology today has advanced to a level wherein assortment of data can be done for each and every granular facet of a business, in real time. Privacy and security is the most important challenges in the big data. To secure existing big data surroundings anticipated to increase risks of breaches and leakages from private data and increased adoption of cloud technology because of the ability of buying, processing ability and storage space on-demand. Revealing traditional and new data warehouses and repositories to the exterior world with the risk to be affected to hackers and harmful outsiders and insiders. Within this paper the existing big data circumstances has been summarized along with issues encountered and security conditions that need attention. Also some existing methods have been explained to demonstrate current and standard guidelines for solving the problems.

Introduction

The most frequently employed solution as regards securing data privacy in a Big Data system is cryptography. Cryptography has been used to protect data for a considerable amount of time. This tendency continues in the case of Big Data, but it has a few inherent characteristics that make the direct application of traditional cryptography techniques impossible. Big data refers to data that is so large in quantity or complexness that current technology struggles to store and process it effectively. Such a need has resulted in the arrival of modern software like Apache Hadoop which uses Map Reduce to process and analyze large amounts of data by parallelizing the process and using distributed hardware [1].Big data consists of both old and new systems to help assess huge amounts of data in a acceptable timeframe and to produce insights which businesses can take action [2]. Technology today has advanced to a level wherein assortment of data can be done for each and every granular facet of a business, instantly. Electronic devices, electric power grids and modern software all generate huge amounts of data, which is in terms of petabytes (1,000 terabytes or 1,000,000 gigabytes), exabytes (1000 petabytes or 1 million terabytes) and zettabytes (1 million petabytes). Together with the development of IOT (Internet of things), every modern electronic gadgets are now-a-days is linked to the internet and is also collecting, creating and saving data which is huge. Total data produced by corporate companies, the internet and devices are anticipated to double to any extent further before next ten years[3]. Software which companies have used for such a long time for handling and examining data are not capable of handling this huge amount of data and therefore needed advanced parallel processing and distributed technology like Map Reduce to get the information analyzed or refined.

Conclusion

While Big Data technology is enhancing daily this mean that the quantity of data combined with the rate of which data is streaming into businesses today is increasing. Private data must be shielded from adversaries and destructive software – both to keep up integrity of the info and personal privacy of very sensitive information. Business decisions produced from data is very important as this what drives future guidelines, hence maintaining accuracy and reliability of data is vital. The security issues must therefore be handled and new novel security methods need to appear that may be modified to Big Data. While software security moves long back computation history, not absolutely all techniques are suited to Big Data. The success of security techniques in safeguarding data and the power of sharing data without privacy concerns will determine the probable of Big Data version to cloud centered environments in future.

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