Big data security and privacy
School of Computing and Mathematics, Charles Sturt University, Melbourne, Victoria
Huge amount of data are being produced every second which are in digital form stored in data centers all over the world. These data are being used by industry and companies to extract useful information using number of analysis tools in achieving goals. The profit is main concern for the organization but there also comes issue related to security and privacy of data. The data that are collected from social media, transactions, emails, complaints, feedbacks and many more. Some data are sensible for individual and should never be fallen into wrong hand. The data security and privacy is required in today’s world which is one of the most important concern. Traditional mechanism is not helpful which such large amount of data which is growing at very high speed. In this paper, the data security and its privacy is being presented and discussed with examples.
Data has always been one of the most important assets in industry and companies. Number of organization uses these data for different analysis purposes and prediction. One of the most important use which known to us is weather prediction. The huge amount of data is being analyzed to make the prediction. In achieving goals, organizations and companies use data and takes it as most important assets. The raise in social network uses, internet of things and many more sources has been producing hug amount of data each and every second. This is era of big data. There is always issues related to new technologies when it comes to security and privacy. This paper gives detailed description about the big data security and privacy in today’s world. There is not only challenges in increasing big data but also its security and privacy is concerned issue. The concerns has to be addressed more with analysis of big data. One of the most important barrier has been security guarantee to individual’s privacy.
The big data is known as framework which allows management of data and its analysis. Number of new technologies are being used and developed in order to extract the valuable information from big data. Traditionally, data were stored in RDBMS format for easy processing. The new tends are being coming and they are semi-structured and unstructured. There are many characteristics of big data and that are volume, veracity, variety, value and velocity. The volume is one the most important in big data as big data are hug. This data are collected from online transactions, social media, live streaming, feedbacks, customers etc. there are different variety in which big data are available and they are semi-structured and structured data. The documents, videos, email, etc. are stored in this. The velocity is the concern as in social media, the production of data is very high. Variability is the inconsistency of data which is high at the peak with data flow. Complexity is one of the important characteristic which is when there are multiple sources of data.
There are four aspects of big data security and they are data privacy, infrastructure security and integrity and data management. This aspects are being used by number of organization for standardization for data security. Traditional mechanism of data security is found to be inadequate. There are number of challenges in big data security and its privacy which is being described in this paper. Through investigation to these challenges are being carried out in sections below.
Challenges and solutions
Big data uses is new technology, there are number of challenges in its security and privacy. Number of organization and companies uses big data and its analysis to achieve its goal in business and profit. The proper mechanism is not being used by them for data security and privacy. It is found that many organization still uses the same traditional mechanism to secure the big data which is out dated and can’t be relied on. The magnification of issues which are related to big data’s increasing volume are found. The challenges in security and privacy of big data are as given described and presented.
Computation in distributed programming framework
The security has to be implemented in distributed programming framework and its computation. The parallel programming framework is being used in distributed programming framework. In large cluster environment, MapReduce is being used which is distributed framework. It is one of tool used to analysis with large data set. It is most popular as it provides distributed computation, load balance and high fault tolerance. There is no build in support that could process the iterative. The improvement in MapReduce was done which could support iterative and it’s known as iMapReduce.
Security concern with non-relational data
NOSQL database are considered to be non-relational database and the data populated by it is non-relational to each other. It is designed for storing hug amount of data and it is open source and distributed. It doesn’t follow ACID and it is found to be horizontally scalable. Being horizontally scalable, it is found to have high performance. This is most popular among web based application and companies. Only few like Cassandra meets security like DSS, PCI. The performance is inversely proportional to security in database.
Transaction logs and secured data storage.
In multiple tired devices the data storage and transaction logs are being stored. It is found to be helpful and controlled for IT manager when the data is being moved manually. The auto tired is necessary when the data is increasing exponentially over time with large volume. The new challenge is being faced as it is not known where the data are being stored by auto-tiering solutions.
Input validation at end point
The company and industry used large amount of data which comes from multiple sources. One of the source is end point devices. The devices provide number of information from the end point devices.
Security monitoring in real time
This is the most challenging for big data security. There are two major factors and they are data infrastructure monitoring itself and data analysis by itself. The alerts that come from security devices are huge in number and it also gives false positive. Due to lack of human capacity, this is ignored. With big data, the problem is increased more.
Data mining, analysis and privacy
The big data might increase the chances of civil liberties, invasions of privacy and other corporate control. The organization uses the analysis for different purpose. In recent research, the analysis of big data done by organization was able to recognize a teen’s pregnancy before anyone in the family. The power of big data analysis is found to be great which shouldn’t be misused. Hence big data privacy and its security is most important to any individual or organization.
Data security using cryptography
The visibility of data can be controlled using two methods where one is by providing limited access and other is cryptography. Both these approach has advantages and disadvantages and found to be effective in cases different from each other. Many organization has combined both the approach and made hybrid solution to solve the problem.
Access control (Granular)
The data can be preventing from unauthorized personal by the organizations and industries. The data can be stolen when it is being shared with other. The use of Granular access control helps to prevent such data privacy and security. The cloud computing also provides huge amount of data which increase the data set. There are number of sources that has policy and legal restrictions. The government has issues the policy for big data security and sharing agreements. The corporate policy also has restriction to data. The granular access control is required to deal with number of increasing security issues.
The goal of real time monitoring system is to alert at the time to attack. Sometimes the attack can’t be detected and hence the audits are being recorded. It is very important to find faults in the system and analyze the attack which was undetected. There are a lot of uses of audits. The data objects are produced more and more which high amount of data. Hence it is challenging to get ideal big data security.
Provenance of data.
In big data applications, the large amount of graphs are produced which increases the complexity. The analysis of such data are intensive and challenging task to do. The large amount of data gives the provenance of metadata. The history of records which are in digital form are required by number of application for security. The data are time sensitive and has to be processed faster with algorithms.
The big data security and privacy is really a challenging task which involves number of technology and hug amount the investment to maintain it. The organization that get benefitted with these data has to maintain certain level the security measure. As the technology is new and there are not much concern from industry it might lead to misuse of individual data. The company uses number of tools to analyses the data and use it for achieving goal of the company. It is everyone’s right to privacy and these data has to be private. The level of authorization and limitation has to be maintained. There are a lot of sensitive information that could lead to disaster in individual’s life. The IOT and social media are along contributor to more than 30% of big data produced every day. Every challenges that are being faced obtaining big data security and privacy is being highlighted in the paper.
In the conclusion, today’s concern is the big data security and its privacy which is being described in this paper. It is analyzed that every 60 seconds only social media produces 98,000+ tweets in twitter, 69,500 status updates on Facebook, 11 million instant messages, 700, 000 Google search and 170+ million email sent. All these data are being analyzed using number of tools by the company and industry for ads, marketing and many more. The challenges are being faced by organization and company not only to analyze the data but also to secure it. The security is the main concern and the challenges associated with it are being presented and described in the paper. There are 10 major challenges and number of characteristic of big data. The analysis of data lead to number of different information which could be used to obtain the goal of the organization and even exceed it. Individual’s data privacy is important which can be misused when fallen into wrong hands. Many different technologies are being used to obtain proper security measures in preventing big data’s security. Big data and its storage are being improved and might lead to future technology to resolve such challenging task.
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