There are a lot of things to be concerned about when your company is working with big data. On top of the list of those things is no less than dealing with several aspects pertaining to cybersecurity. The presence of hacking technologies purported by cybercriminals wanting to infiltrate your system will surely be one of your concerns as well. The truth is that the bigger the data you are working with, the greater the risks involved. However, there are ways in which you can leverage big data analytics without compromising on cybersecurity:
Ensure Distributed Computing Frameworks are Secured
Distributed computing frameworks use parallel computation across more than one user. This creates opportunities for security breaches. If your company frequently needs to analyze big data, it is pertinent that you identify any unreliable or malicious computers and processors.
Let’s take a look at two methods for ensuring the trustworthiness of your organization’s computer systems. The first technique is Mandatory Access Control (MAC). MAC constraints each worker to a limited set of tasks. Trust establishment, on the other hand, stringently authenticates workers. This means that only individuals with a “Masters” role can gain access to certain properties. Next, worker properties are regularly reviewed to ensure they conform to predefined standards.
Data Encryption is a Must
If you want to preserve the integrity of your data, you must make sure that data encryption will be made available at all levels. When data moves from one point to another or is being stored at a certain location, it has to be encrypted at all times for security reasons. Using SSL encryption will be best for data in transit. Alongside this, there should be an assurance that only trusted computers will gain access to encrypted data. Real-time log monitoring will also help spot anomalies.
Privacy Must be Preserved at All Times
Switching to the cloud or any better way to leverage your big data analytics will require preservation of privacy at all times. You can develop several approaches like differential privacy. It is a proven formal model that can help you manage large data infrastructure. There is also homomorphic encryption, which is one of the emerging technologies that can permit analytics to work with encrypted data. Take note that it is important to comply with the legal requirements of the particular country, where you are operating, when you are implementing privacy policies.
Enforce Access Controls on Individual Objects
Enforcing access control over these objects gives you a choice between both identity-based and attribute-based encryption. In an identity-based system, plain texts are encrypted and can only be decrypted based on the identity of the entity. Attribute-based systems, on the other hand, use attributes to encrypt data instead of relying on identities.
Data Management Should be Secure as well
Data center solutions for big data analytics must always be geared at using technologies that are not just secure but will also be capable of responding to the demands of the company. Among some of the solutions that you can make use of are: regular conduct of audits, asymmetric encryption, and hash chaining. All these can be used when creating digital signatures. Collusion attacks can also be addressed by using different levels of decrypting and encrypting policies together with the use of digital rights management.
As you have seen, there are a lot of ways that will help leverage big data analytics without compromising on your company’s cybersecurity. It is just a matter of discussing your options with ANEXIO. We are a trusted, large-scale Infrastructure As A Service (IAAS) provider that will help secure your data in the best way possible.