Big Data Security

This is the era of big data. Big data includes huge volumes both structured and unstructured data that businesses accumulate in their day-to-day working. This data can analyzed to enable understanding of consumer behaviour, in cutting costs, in new product development, time reductions and in smart decision making. With the growing use of IoT and other connected devices, the outpour of data has been humungous and organization, management and analyzation of data assume great importance. And with this growth another issue that crops up is the issue of data security and protection of users’ privacy.

Big data experts have pointed out the following challenges when dealing with the security challenges of big data:

• Checking fake data generation

  Fake data can be generated by cybercriminals in order to diminish the quality of data. For example, if a manufacturing unit makes use of sensors to identify processes that are malfunctioning; the cybercriminals can take control of your system and fill it with fake data like incorrect temperatures. In this way, the organization can fail to track trends accurately and may suffer great damage. Sometimes, the Fake data generation software that is meant for testing can be misused.

• Possibility of mining sensitive information

  Big Data Protection usually incorporates perimeter-based security. In this method, all point of ‘entry and exit’ are carefully secured. But still the IT specialists can do anything with your system and it is difficult to keep track of their working. Dishonest IT specialist can be bribed by your business rivals to reveal sensitive information. If information regarding a new product launch by any company is leaked it can lead to serious business setbacks. While this issue of sensitive information mining is a threat to Big Data handling, often extra parameters are used to give added protection to data.

• Checking presences of untrusted mappers

  After the collection of data, it is made to go through a process of Parallel Processing. The method used in this processing is called MapReduce. Under the ‘MapReduce’ paradigm, the data is first ‘split’ and then mapped. After this, the data is shuffled and reduced. After splitting, the data is then allocated to particular storage options. If any cybercriminal gains access to your system and alters the settings of the mapper’s code, the data processing may be ruined. This will result in production of faulty results. Your sensitive information can also be leaked in this way. Due to the absence of proper security of Big Data, which relies primarily on perimeter security system, gaining such access to any mapper code is not that difficult.

• Problem of granular access control

  Granulated access control refers to the permission of granting access of data to particular group of users only. In such a system, some parts of sensitive information are under restrictions but other parts. For example, in case of healthcare organizations, the personal details of the patients may be hidden to users but may be allowed to be accessed by medical researchers. Now in such a system and with continuous growth of data, it becomes difficult grant control of information to a particular section. In such cases, the parts of needed data sets are separated from the comprehensive data and offered to the designated users as a new ‘whole’.

• Problem of cryptographic protection

  Encryption is an excellent way to secure private information but when it comes to handling of Big Data, this method of security is generally ignored. This is so because sensitive data is mainly stored on cloud platforms that do not bear any encrypted protection. Also, encrypting and then decrypting huge volumes of data takes a lot of time and defeats the purpose of faster processing of data and so most of the times, this method is ignored because of the inconvenience that it causes.

• Difficulties of Data Provenance

  Data Provenance refers to the historical account of all the data. It is also called metadata. It helps to identify the authenticity of data and also its quality. Over time, the collection of data can assume gigantic proportions and become difficult to handle and store. As such security of such Data Provenance is an issue in itself.

• Absent security audits

  Security audits of Big Data are a solution to the problem of security gaps. Such audits should be conducted at regular intervals to identify and address security issues. But in reality, this recommendation is rarely met. Already Big Data handling involves many challenges and this one adds to the list. Also, lack of resources, qualified audit personnel and lack of time make the organizations overlook this aspect.
  
  

• High speed of NoSQL database evolution shifts the focus from security

  In Big Data Science, NoSQL databases are quite popular. Since these databases are continuously being honed with new features, security aspect is often ignored and pushed to the background. Also, NoSQL database do not offer embedded security in the database itself. Owing to the unstructured nature of data, the distributed environment and cost of security, security aspect tends to be ignored.
  
  

Surely, when it comes to Big Data Science, security is a big challenge. But the IT specialists are working day and night to solve these issues. Many Professional Big Data consulting firms offer excellent support when it comes to protection of Big Data. Governments are also coming forward to establish legal structures and policies fort protection of data. Hopefully in the future the Big Data advantages will overcome all the challenges that it faces today.