Security issues: A major concern with the Growing Dependency and Usage of Big Data concepts

With the advent of technology in present era all most all organizations, large and small enterprises, private companies, almost all sectors whether Government organizations, private organizations, sector related to healthcare, airlines industry, sensors, Education sectors, Sales and marketing department of almost all companies, various government projects of digital era are using the booming technology related to big data and working rigorously on big data project.

Demonetization is the current source which has generated humongous amount of data. Analytical engineers are working day and night to know the facts and details of black money defaulters. If analytics are not applied on the data on time, data becomes stale. Presently the data is generated by three explosions; cloud Computing Explosions, Data Explosions, and Conversion Explosion.  This exploded data is stored in large repositories which are also increasing every second where Data is stored in peta-bytes, zeta bytes.

Big Data Systems: Big data systems can store very large amounts of data; can manage that data across many systems; and provide some facility for data queries, data consistency, and systems management.

The challenge is not to manage a boatload of data – many platforms can do that. And it’s not just about analysis of very large data sets. Various data management platforms provide the capability to analyze large amounts of data, but their cost and complexity make them non-viable for most applications. The big data revolution is not about new thresholds of scalability for storage and analysis.

Big Data as an specific Technology:

Big Data as a specific technology is not any Hadoop HDFS or Lustre or Google GFS or shard storage system. It is more than managing big data sets. It is not a Map Reduce cluster as its more than how you query large data sets.

Big data is an application with all combined traits of different technologies together which attracts developers, data managers and large scale data analysts. It’s a collection of plethora of cost effective technologies with different attributes and capabilities, works together to give effective and result oriented results.

Big data as any data repository is able to handles large amounts of data stored in distributed and redundant storage. It can perform parallel task processing and easily accessible as a commercial or open source product. It is extensible with basic capabilities which can be augmented and altered easily. Big data revolution is basically built on three pillars, big, cheap and easy data management which enhances its ability to scale data store at greatly reduced cost and analyse data easily, effectively in a faster manner with all complex data type. It provides all characteristics which are not available with traditional databases.

Hadoop Framework: The big data systems uses Hadoop framework i.e. most big data systems actually use one or more Hadoop components, and extend some or all of its basic functionality. The components of Hadoop Framework can be explained through its working and divided into 5 layers:

Layer 1: Data Storage: HDFS

Layer 2: Data Processing: Map Reduce

Layer 3: Task and Resource Management: YARN

Layer 4: Data Access: PIG

Layer 5: Orchestration: HBase

The Hadoop framework is much like a LAMP stack. Normally these pieces are grouped together, but you can mix and match, or add onto the stack, as needed. For example, there are optional data access services like Sqoop and Hive. Lustre, GFS, and GPFS are data storage alternatives to HDFS. Or you can extend HDFS functionality with tools like Scribe. The entire stack can be configured and extended as needed. As this new technology enables all to collect, manage and analyze their data lying in humongous amount in the repositories and take better and efficient decision for their betterment and growth. It has significantly changed the nature of analytics from descriptive analytics to Disruptive Analytics to Predictive Analytics so that this humongous data can be leveraged in global business to improve its economy. As the technology is benefiting the company in terms of growth prospects the repositories are stuffed with sensitive data which emerges the issues of security. It is a matter of big concern as all sensitive information of companies or individuals are stored in repositories which can be misused and can promote to fraudulent. Lots of demographic information of individuals is also stored in big repositories under UIDAI scheme. At the same time as individuals adhaar number is linked with various govt schemes provided for citizens, data is laundering freely. If a small human stupidity get take lace due to unawareness, it’s very easy to get victim.

With the increased adoption of cloud based, web based and mobile based applications sensitive data has become accessible from different types of platforms easily. Especially these platforms are highly vulnerable to hacking, especially if they are low-cost or free.

Hackers and crackers are keeping their eyes on this flow of data, anytime by any means they can catch your personal information and can do all malicious attacks, frauds and can perform mischievous activities which can harm personally or financially. This is the reason the protection of private and confidential information gains more and more attention. Big Data Security is the most highly ranked priority in the IT strategies, in case of big data the way data is exploding protection of information is of crucial importance. Hence we can say a lack of data security can lead to great financial losses and reputational damage for a company. In case of continuously exploding big data losses due to poor IT security can exceed even the worst expectations.

Challenges of Big Data Security:

·         Single level protection in most distributed systems

·         Evolvement of NoSQL without security feature

·         Requirement of additional security measures in automated data transfer

·         Validation of information received, whether it remains trustworthy and accurate.

·         Mining personal information without asking user permission or notifying them.

·         Lack of division of different level of confidentially within the company.

·         Lack of routine audits to be performed on Big Data.

·         Inconsistent monitor and track of origin of big data.

Antivirus industry is working together and continuously from years to deal with malicious attacks and to provide maximum gains. The big data security can be improved by focusing on application Security, rather than device security, should keep isolated devices and servers containing critical data, by introducing real time security information and event management, by providing reactive and proactive protection.

Ms. Arpana Chaturvedi

Assistant Professor

Dept. of Information Technology