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One of the obvious challenges that big data security encounters is the protection of users’ privacy. Big data contains lots and lots of personal information and thus users’ privacy is a big concern.

Since huge amounts of data are stored, a security breach in big data can have disastrous effects and consequences than a regular security breach we often see in the media. This is because a security breach in big data will hit a greater number of people, and the consequences they face could be both reputation-wise and tremendous legal repercussions.

Before producing the information for big data, companies have to make sure that they have the right balance between the data and privacy. It should be properly anonymized, eliminate unique identifiers if it has any. Removing unique identifiers alone is not enough to guarantee that the information will remain anonymous. There are chances that the anonymized information could be cross-referenced with the available ones followed by de-anonymization techniques.

The utility of big scale cloud infrastructures, with diverse software platforms, spread across a network of systems, also enhances the security breach of the entire system.

But how do these big data problems arise?

It is just not about the huge amounts of data that causes security breach and privacy issues. The continuous streaming of data, large-scale data migration from one system to another, different cloud-based data storage practices, and several kinds of references all have their own loopholes and issues.

Big data has been existing for many decades now. However, the significant difference is that earlier only large-scale organizations used to collect data because of the huge amounts of information and expenses, but now it is used by almost every medium-large scale organizations to only collect data also for various purposes.

Low-cost cloud-based data collection procedures, along with robust data processing software frameworks are allowing companies to simply mine and process big data. As a consequence, various security and privacy-compromising scenarios have appeared with the large-scale integration of cloud-based data storage and big data.

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To better understand the privacy and security concerns of big data, here are top 4 challenges that are faced by big data today:

Securing Transaction Data

Frequently, the transaction data and other sensitive information stored in storage means might have multiple tiers but that is not sufficient. The organizations who have to guard these data storages against unauthorized access.

Validation of Endpoint Inputs

Endpoints are a crucial part of big data collection. They give input data for storage, processing, etc. so it is highly essential to make sure that only authentic endpoints are in use. Every network system should be free from unauthorized and spiteful endpoints.

Required Granular Auditing

A frequent auditing is required along with constant monitoring of data. Right analysis of the different sorts of transaction data and logs can be beneficial and used to detect attacks and spying.

Securing Non-relational Data Source

NoSQL type of data stores can have potholes that raise security and privacy concerns. Such loopholes lack the ability to encrypt data when it is being stored or during logging of data. If you want to know more about big data and logging data management visit Mezmo for more information.

Not just big data, any advanced concept can have loopholes in the form of security and privacy issues. But how you overcome or eliminate such privacy concerns is what matters. However, there are some good practices for managing big data from a security point of view. And here are they:

  1. If you are stocking your big data information in the cloud, then make sure that your cloud provider has good enough security mechanisms in place. Also, make sure that the provider carries out regular security inspections and agree fines in case sufficient security standards are not met.
  1. Develop a proper access control policy such as allowing access to authorized people users.
  1. Secure the data: Both the raw information and the result from analytics should be properly secured. Encryption should be utilized to make sure that no confidential data is leaked.
  1. Secure communications: Data in transport should be appropriate to make sure its confidentiality and integrity.
  1. Real-time security supervising: Access to the information should be supervised. Tools related to threat intelligence should be deployed to prevent unauthorized access to the confidential data.
  1. SSL certificates offer great security and protection for e-commerce websites. It encrypts all the communications that happen between a website and browser. So consider buying SSL certificates.
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Are there any technological solutions that are available to assist in securing big data?

The chief solution of big data security and privacy breach is the proper utility of encryption. For instance, attribute-based encryption is a key that can assist in providing great access control of encrypted data.

Another thing is anonymizing the data. It is equally essential to make sure that privacy issues are addressed. Also, ensure that all confidential data is excluded from the records obtained.

When it comes to security issues for a big data project, real-time security monitoring is also helpful. It is crucial that companies keep a note of each access that takes place to make sure that is no unauthorized entry. To handle it more efficiently, deploy some good tools of threat intelligence that detect security attacks and the companies can react to those malicious threats accordingly.

There are a lot of strategic and tactical policy strategies that are in existence which do the same. Companies should regularly run a risk assessment over the collected data. They should ensure that the customer information if collected any, should be kept in private and develop strategies that secure the data and the right to privacy of their clients.

Big data is here to stay with the organizations and within the organizations. It is highly impossible to think about the next application without it utilizing data, providing new forms of data, and including data-driven algorithms. As big data is indeed big, several robust solutions should be introduced in order to secure and protect every little gain of the data. Data storages should also be protected by ensuring that there aren’t any loopholes. Last but not least, real-time security must be allowed during the first stage of the data collection. All this will definitely ensure the customers’ privacy is well maintained.