• Richard Blech

Big Data Encryption

There are many use cases for big data across the various industries. Organizations apply predictive analytic algorithms, data visualizations and other types of data analytic methods to data such as metrics pertaining to customer behavior or measurements related to device power consumption to derive actionable insights. In the digital age, acquiring big data capabilities can give organizations an immediate competitive advantage.

However, data security remains is a persistent concern. Organizations have to contend with malicious actors who are actively pursuing big data assets to hold it for ransom or to stockpile it for decryption in future when the right capabilities become available. There are also the increasingly stringent industry and federal regulations regarding data privacy and cybersecurity that require compliance.

The multilayered security framework that protects big data systems should leverage the latest and most effective technology solutions to create a barrier that minimizes the risk of exposure of sensitive data. Ideally, it should create an environment that allows for the safe storage, import, analysis and export of that data. Encryption has always been a critical part of this security framework, and with the amount of data that is being generated, it is more important than ever.


In the context of encryption, there are some factors to keep in mind regarding the concept of big data. Big data is a significant volume of data. It is bulk datasets that are routinely measured in many multiples of petabytes, zettabytes or even exabytes. For comparison, the volume measurements for non-big data usually do not exceed gigabytes or terabytes. Not only can big data volumes be extremely difficult to manage without the right technology, but the expansive volumes also create an immense attack surface that can be vulnerable to malicious attacks. The sheer amount of big data can actually increase cybersecurity risks.


China’s mass surveillance and big data harvesting operations have been collecting unencrypted data (low-hanging fruit) as well as encrypted data (expecting to decrypt later with quantum capabilities) of detailed personal and highly sensitive information on a massive scale for multiple reasons: to boost its economy, advance its technology and to support its espionage efforts.

China has set as one of its strategic goals to achieve dominance in artificial intelligence. What you need to feed artificial intelligence algorithms is lots and lots and lots of data, i.e big data that they aggregate with their super computers and soon to be quantum computers. The DNA collection by Chinese companies, should be seen as part of a comprehensive effort to vacuum up millions and millions of records on U.S. citizens. Most Americans have probably had their data compromised by the cyber intelligence units of the Chinese government and Chinese military intelligence. The ongoing theft of American technology and big data has given China a huge economic lift, and inflicted great damage on the U.S. China needs access to valuable intellectual property to fuel that economic engine for which has permitted the unabated theft US innovations and is the greatest transfer of wealth in history.

The security issue of massive data volumes is compounded by the widely varied formats of the data. The information in big data systems includes structured, unstructured and semi-structured formats that are sourced from inside and outside an organization and that is generated by users and machines. These data types, such as device data, video, audio files, images, sensor data, social media, etc., have to be in encrypted form during the transmission from the source to the platform. This can further complicate the already difficult task of securing the distributed architecture of a big data system.

Transmitting big data is another point of concern. Securing big data should not correlate to lags in time or application performance. This is a particular drawback of using traditional encryption technology on big data. Those measures were designed for encrypting for small-scale, fixed data on relatively isolated networks. Modern cyber infrastructures, especially those that include big data environments, require security measures that are agile enough to secure data wherever it is and regardless of how fast it is moving.


Encryption technology like what is offered by XSOC CORP is necessary for securing big data at rest and in transit because it can rely upon for impenetrable protection now and be ready for the post-quantum threats, even in scenarios in which weak authentication and authorization techniques have been applied. Organizations can use the quantum-safe encryption to securely store and transmit expansive volumes of datasets in any format. XSOC solutions provide these functionalities without negatively impact application performance or transmittal speeds.

For example, the FIPS 140-2 validated XSOC Cryptosystem can be used to encrypt non-relational databases, such as NoSQL. It can also be used to protect relational database management systems and distributed file systems.

The justification for big data and the complexity and costs of creating big data environment is the ability to analyze immense volumes of complex data. The intelligence that is produced from the analytics is often exported to reports and applications and have to be encrypted as well. XSOC’s EBP allows organizations to establish the secure connections and communications tunnels that can be used to safely transmit or share extremely large datasets at high speed. The quantum-safe encryption can be applied directly to output data from analytic tools for long-term storage and preservation.

It has to be noted that homomorphic encryption has emerged as an option to allow the analysis and computation of big data without having to decrypt the sensitive information and keep the data protected at all times. However, the technology is still in its early stages and the amount of time homomorphic encryption requires to execute simple computations makes its wide-scale implement impractical—at least for now.

However, XSOC’s encryption solution for archival data allows for encrypting of data at the file level and storing in one place while placing all of the decryption keys for said data in a vault in another location. This will permit the homomorphic style of querying of encrypted data from selective metadata only and matching it with the appropriate key used to decrypt only the selected file by the authorized and authenticated user.


When encrypting big data, the technology being used has to be able to efficiently handle datasets of all sizes and formats and do so without sacrificing functionality. Get in touch with us today to learn how XSOC CORP can help you organization leverage the benefits of big data security.