To economically capture, store and analyse large volumes of structured, semi-structured and unstructured data at scale. A new generation of technologies and architectures centred around machine learning, artificial intelligence and cloud computing were designed to capitalise on.
The migration of businesses, consumers and devices, onto the internet. And the corresponding explosion in the amount and variety of data.
However, there is a direct relationship between, increasing data production, and the probability of privacy breaches for organisations and individuals.
At Flux Insights data privacy is of paramount importance.
As organisation, collect and process an array of data formats from different data sources. On their consumers, companies are mandated to safeguard the information they hold on their customers from unsolicited disclosure.
Failure, to meet the privacy expectations of customers may materially impact the reputation and performance of a firm negatively.
There are several anonymisation techniques, organisations may deploy when processing and publishing large volumes of customer data.
Such as the removal of identifier, quasi-identifier, sensitive and non-sensitive attributes.
This may go some way to mitigating the ability of nefarious actors from gaining access to sensitive and highly confidential information.
As organisation conduct large scale analytics, and store data in the cloud. On thousands or even millions of customers.
The maintenance of the integrity, confidentially and availability of the data traversing their ecosystems is paramount.
To address this dilemma, firms may deploy encryption techniques such as attribute based, identity based or homomorphic encryption techniques. To preserve consumer privacy.
Also, once insights are obtained from the analysis of the data, and is disseminated internally. Employees can trust the quality of the intelligence derived from analytic activities.