Companies must not overlook big data security needs

John McMalcolm
- Technology - Apr 19, 2015

In recent years, the amount of data generated has been increasing at an exponential rate, and many companies in Canada have set up big data repositories to store, manage and analyze their data.

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Big data can be greatly beneficial to businesses, because it allows them to obtain new insights from their data.

However, big data is still an immature technology, and it poses many challenges for businesses. One of the main concerns with big data is security.

Here is a look at how Canadian companies can overcome the security issues that are associated with big data….

Benefits of Big Data

Big data analytics can be useful to companies in a wide range of industries.

According to an article entitled "The Internet of Things: Big Data is About to Get Bigger", this technology can be applied to the agriculture, transportation, construction, manufacturing, food processing, health care, energy and other sectors.

Businesses can use big data analytics to identify and minimize problems, develop more effective marketing strategies, improve their products or services to better meet customer needs, enhance communication both internally and externally, and monitor business activities more thoroughly.

The new insights they gain from big data can help them improve their decision-making processes and reduce their risks significantly.

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Understanding Big Data Security Challenges

Reducing big data security risks is a daunting task because of a number of reasons.

First of all, big data repositories usually contain data from a variety of sources, each of which may have its own security policies. This makes it difficult for companies to balance security across all data sources while maintaining appropriate levels of accessibility.

Additionally, since big data environments are geographically distributed, companies may have trouble standardizing physical security controls across every accessible location. Those that have a large number of servers may not be able to configure their servers consistently, and therefore, some servers may remain prone to attacks.

Another reason why big data poses security risks is because big data programming tools, such as Hadoop and NoSQL, were not initially developed with security in mind.

How to Make Big Data More Secure

Since big data is a relatively new technology, there is no set list of security measures that will work for every company.

However, there are general recommendations that can help businesses reduce security risks significantly.

They include:

• If you are implementing big data analytics in the Cloud, you have to make sure that your Cloud service provider has adequate security mechanisms. The provider should carry out security audits regularly and agree to penalties in the event of failure to meet security standards.

• Create an access control policy that makes data accessible to authorized users only.

• Take effective measures to protect both raw and analyzed data, and use encryption to prevent leakage of sensitive data.

• Protect data in transit adequately to maintain its integrity and confidentiality.

• Monitor data access with real-time security monitoring.

Overcoming big data security challenges is not an easy task.

However, with the right combination of security policies and processes, it is highly possible for Canadian businesses to prevent big data breaches.

About the Author: John McMalcolm is a freelance writer who writes on a wide range of subjects, from social media marketing to Cloud computing

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Comments(3)

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Ulf Mattsson    Apr 20, 2015
It is critical to protect sensitive data wherever it is stored, including the enterprise and the cloud.

I found great advice in a Gartner report, covering enterprise and cloud, analyzed solutions for Data Protection and Data Access Governance and the title of the report is "Market Guide for Data–Centric Audit and Protection.”

Gartner also defined the “Cloud Encryption Gateway”, which performs encryption, tokenization or both before the data is sent to the cloud. Cloud encryption gateways typically provide a choice of various encryption and tokenization algorithms to meet the requirements of different data sensitivity levels.

Ulf Mattsson, CTO Protegrity
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Andreea Panainte    Apr 20, 2015
Actually, there are NoSQL databases built with security in mind. An example is CryptonorDB (privacy aware cloud – mobile database): it safeguards the data by encrypting it. CryptonorDB manages the storage of encrypted data (data is encrypted before upload on client device and decrypted right before is used). Furthermore, only the owner of the data can manage the decryption key.
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Ilya Geller    Apr 19, 2015
There is no Big Data.
Being structured data becomes database, which can easily be managed in any requested way.
I discovered and patented how to structure any data: Language has its own Internal parsing, indexing and statistics. For instance, there are two sentences:

a) ‘Fire!’
b) ‘Dismay and anguish were depicted on every countenance; the males turned pale, and the females fainted; Mr. Snodgrass and Mr. Winkle grasped each other by the hand, and gazed at the spot where their leader had gone down, with frenzied eagerness; while Mr. Tupman, by way of rendering the promptest assistance, and at the same time conveying to any persons who might be within hearing, the clearest possible notion of the catastrophe, ran off across the country at his utmost speed, screaming ‘Fire!’ with all his might.’

Evidently, that the phrase ‘Fire!’ has different importance into both sentences, in regard to extra information in both. This distinction is reflected as the phrase weights: the first has 1, the second – 0.02; the greater weight signifies stronger emotional ‘acuteness’.
First you need to parse obtaining phrases from clauses, for sentences and paragraphs. Next, you calculate Internal statistics, weights; where the weight refers to the frequency that a phrase occurs in relation to other phrases.
After that data is indexed by common dictionary, like Merriam, and annotated by subtexts.
This is a small sample of the structured data:
this - signify - <> : 333333
both - are - once : 333333
confusion - signify - <> : 333321
speaking - done - once : 333112
speaking - was - both : 333109
place - is - in : 250000
To see the validity of the technology - pick up any sentence and try yourself. After that try a paragraph?