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What Is Big Data Security?

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By: Nihad Hassan

August 18, 2021

Digital transformation is rushing to occupy all aspects of our lives. Organizations of all sizes and across all industries are utilizing digital solutions to store and process data. Nowadays', most data are created digitally and never find their way into papers. The volume of digital data is growing at an explosive rate and will reach an unprecedented size. According to bigdataldn, 2.5 quintillion bytes of data are created each day. A Statista report found that the total amount of data created, captured, copied, and consumed worldwide is forecast to increase rapidly, having already reached 59 zettabytes in 2020. The massive volume of digital data created daily makes it common for large organizations to have Terabytes – and even Petabytes – of data in storage units and data servers. The sum of data, both structured and unstructured, acquired from different sources, is now known as Big Data. Big Data volume is too large and complex for traditional data management tools to store or process.

Organizations collect data from various sources, like financial transactions, social media, industrial equipment, and the internet of things smart devices, to name only a few. Big Data offers numerous benefits for organizations' work, such as improved decision-making, forecasting future events, mitigating risks, data-driven marketing, and enhancing the delivery of products/services promptly.

What is big data security?

Big Data security is a general collective term used to describe the tools, techniques, and methods used to protect Big Data and the associated analytics processes/tools from all threat actors. Big Data security is concerned with protecting data from malicious access or unauthorized modification/destruction attempts originating from online or offline environments like other cybersecurity domains.

When part of an organization's big data is stored in the cloud, there are additional challenges to secure Big Data in the cloud.

Threats against Big Data

Different threats are affecting Big Data, whether it is stored on-premise or in the cloud:

  1. Theft of information.
  2. Ransomware attacks that encrypt stored data.
  3. Distributed Denial of Service (DDoS) attack resulting in crashing the storage servers, thus preventing access to it.

The most significant challenge of Big Data security is securing the personal data of users. Big data can potentially contain a huge amount of Personally Identifiable Information (PII), such as personal customer info, credit card info, patient records, and contact details. Failing to protect such sensitive data can have serious legal, reputation, financial, and regulatory consequences.

The diversity and amount of data result in a data breach with more significant consequences than the data breaches we hear about regularly in the press. This is because a Big Data breach impacts many people, and its legal consequences will span more than one country. These breaches will undoubtedly make an organization liable for breaching many data protection regulations such as PCI DSS, HIPAA, and the European General Data Protection Regulation (GDPR).

How to approach Big Data security?

Big Data security requires an organization to look at all areas affecting its data and mitigate any risks to Big Data in storage, process, or transit. The following list gives the most important measures to secure Big Data while it is in any of its three statuses:

  1. When retrieving Big Data for analysis, it is essential to remove any identifiable information that can point to its owner in any way. Any personal or sensitive info must be stripped first, and only the general information should remain. Anonymizing Big Data is complex and subject to different challenges. For instance, can the resultant data be de-anonymized again after combining it with other data sources?

  2. Big Data should be stored encrypted on storage servers. However, this creates a challenge when needing to process this data, especially when using cloud-based analytics tools, as processed data need to be decrypted first. Various attacks affect data during runtime (or process). To mitigate this problem, organizations use Fully Homomorphic Encryption (FHE). FHE is a type of encryption that allows analytical functions to work on encrypted data and generate encrypted results as if they were processing plaintext data.

  3. Deploy robust security solutions to protect the Big Data; the most important one is installing a firewall on the gateways. This way, all input and output connections are strictly filtered and monitored to prevent unauthorized access to stored data.

  4. Set access control mechanisms in place. An organization should only allow a limited number of users to access the Big Data repository. Physical and logical security controls must be enforced to protect Big Data servers and other storage units.


Big Data security is a relatively new concept; securing Big Data is similar to securing data in data centers and the cloud. Enforcing encryption for data at rest, setting access roles so only authorized people and systems can access Big Data repositories, protecting Big Data while in transit via encryption, and employing FHE encryption scheme to protect Big Data when processing it in the cloud are all important countermeasures to protect and secure Big Data for any organization.

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