Big data privacy, security challenges and solutions

The huge advances predominating in today’s technological scene has brought opportunities for big data to improve businesses across industries and boost markets. Its role has been increased to a degree where obtaining value from received information has proven to be valuable to all sizes of organisations. Big data supports organizations that have critical reliability in presenting the world a better place do their jobs.

The greatest challenge for big data from a security angle is the shield for the privacy of users. Big data generally holds huge amounts of personally identifiable information and therefore user’s privacy is a large matter to be taken care of. Due to the huge volume of data stored, breaches affecting big data can have more destructive results than the data breaches we normally see in the press. This is due to a big data security breach will probably attack a much larger number of people, with results not only from a reputational point of view but with large legal repercussions.

When generating information for big data, companies have to assure that they have the right balance between the efficiency of the data and privacy. Before the data is saved it should be sufficiently anonymized, eliminating any unique identifier for a user. This in itself can be a security challenge as removing unique identifiers might not be enough to guarantee that the data will prevail anonymously. The anonymized data could be cross-referenced with other accessible data following de-anonymization procedures.

When storing the data, organizations will face the difficulty of encryption. Data cannot be transmitted as encryption by the users if the cloud requires to execute operations over the data. A resolution for this is to use FHE (Fully Homomorphic Encryption) which allows data stored in the cloud to perform operations over the encrypted data so that new encrypted data will be generated. When the information is decrypted, the results will be identical as if the actions were brought out over plain-text data. Therefore, the cloud will be able to perform operations over encrypted data without information of the underlying plain-text data. While handling big data, a relevant challenge is how to fix ownership of data. If the data is collected in the cloud a trust line should be secured between the data owners and the data storage owners.

Sufficient access control mechanisms will be key to guarding the data. Access control has traditionally been provided by operating systems or applications restraining access to the information, which typically reveals all the data if the system or application is hacked. A beneficial method is to preserve the information using encryption that only supports decryption if the entity attempting to access the information is approved by an access control policy.

A further difficulty is that a software usually used to store big data, such as Hadoop, invariably doesn’t come with user authentication by default. This makes the obstacle of access control worse, as a default installation would leave the data open to unauthenticated users. Big data solutions usually rely on conventional firewalls or implementations at the application layer to limit access to the information.

Big data is a comparatively new concept and hence there is no list of best applications yet that are widely accepted by the security community. Still, there are a number of general security guidance that can be applied to big data:

big data security challenges

Big Data Security Solutions

Vetting cloud service providers: If you are preserving (storing) your big data in the cloud, you must assure that your provider has sufficient security mechanisms in place. Make sure that the provider executes periodic security audits and accept penalties in case that adequate security standards are not met.

Formulate a fit access control policy: Create policies that provide access to authorized users only.

Secure the data: Both the raw data and the outcome from analytics should be adequately protected. Encryption should be adapted accordingly to prevent sensitive data leak.

Preserve communications: Data in transit should be appropriately guarded to secure its confidentiality and uprightness.

Practice real-time security monitoring: Access to the data should be observed. Threat intelligence should be used to prevent unauthorised access to the data.

Technological solutions are ready to help protect big data and secure that it is collected and used well. The principal solution to securing that data continues protected is the adequate use of encryption. For example, Attribute-Based Encryption can assist in giving fine-grained access control of encrypted data.

Anonymizing the data is also necessary to ensure that privacy concerns are addressed. It should be ensured that all delicate information is eliminated from the set of records collected. The real-time safety monitoring is a fundamental element of a big data project as well. It is relevant that corporations control access to secure that there is no unlawful access. It is also essential that warning intelligence is in place to ensure that more complex attacks are identified and that the organizations can respond to alerts equally.

Organizations should operate a risk-evaluation over the data they are gathering. They should examine whether they are collecting any customer data that are meant to be kept private and establish adequate policies that preserve the data and the right to privacy of their clients.

If the data is shared with other organizations then it should be considered how this is done. Intentionally published data that transforms to violate privacy can have a huge influence on an organization from a reputational and financial point of view. Organizations should also carefully analyse regional laws around handling customer data, such as the EU Data Directive.

Several big data solutions look for emanating models in real time, whereas data warehouses usually concentrated on occasional batch runs. In the past, large data sets were collected in highly structured relational databases. If you wanted to look for sensitive data such as health records of a patient, you knew specifically where to look and how to obtain the data. Also, eliminating any identifiable data was easier in relational databases. Big data makes this a more complicated process, particularly if the data is disordered. Organizations will have to track down what pieces of information in their big data are delicate and they will need to fully divide this data to assure agreement.

big data privacy

Another hurdle in the case of big data is that you can have a big category of users each needing access to a distinct subset of information. This means that the encryption solution you wanted to preserve the data has to exhibit this new reality. Access control to the data will also require to be more granular to assure people can only obtain the information they are approved to see.

Companies guarantee that they are compliant with the necessary regulations while using big data. The main challenge acquainted by big data is how to recognise delicate pieces of information that are collected within the unstructured data set. Organizations must make sure that they isolate sensitive information and they should be ready to confirm that they have adequate processes in place to achieve it. Some merchants are starting to offer compliance toolkits created to serve in a big data environment.

Anyone utilising third-party cloud providers to store or process data will want to assure that the providers are complying with guidance. Security is a manner, not a result. Accordingly, organizations using big data will need to introduce enough processes that help them productively manage and protect the data.

The traditional information lifecycle management can be used to big data to assure that the data is not being stored once it is no longer required. Also, policies related to availability and restoration times will still utilise to big data. However, organizations have to analyse the volume, velocity and complexity of big data and improve their information lifecycle management accordingly.

If a fair governance structure is not applied to big data then the data received could be misleading and cause unforeseen costs. The foremost obstacle from a governance point of view is that big data is a comparatively new idea and therefore no one has designed procedures and policies.

The test with big data is that the unstructured nature of the knowledge executes it even difficult to classify, model and map the data when it is taken and deposited. The obstacle is made least by the fact that the data usually comes from outside sources, often creating it complex to confirm its correctness. If they apprehend all the knowledge available they risk wasting time and support processing data that will add few or no value to the industry.

All these technological leaps across many industries have solid support in the form of big data. Progress will remain to help build a larger society through smarter processes. But to eventually benefit from these trends, organisations must fully understand how they are practised and how they can help accomplish the business intentions.

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