Today, homomorphic encryption could empower competitive financial firms with big data-related concerns by providing alternatives to its sources and innovating collectively to create their own proprietary market data products.
Although advances in data analytics have enabled businesses to acquire expanded insight into large structured and unstructured datasets, these advances have limited privacy and misappropriation risks.
Having greater control over the life cycle of data and confidentiality agreements has alleviated these risks, but outsourcing sensitive or regulated data processing components to third parties is still broadly viewed as fraught with risk.
Any third party, including competitors, could share all sensitive data or data processes and algorithms, subject to the provider’s controls. However, it would open unimagined avenues of enterprise collaboration, integration, and specialisation.
Homomorphic encryption voids this significant gap. As commercial viability is still a challenge, compelling use cases are emerging. In the coming years, any organisation that tends to become a center of excellence in big data analytics will be left with no choice but to embrace homomorphic encryption.
Encryption is a digital locker where information is secured until locked inside. Plaintext data is converted to ciphertext, applying a sufficiently complicated algorithm to make the data unreadable without a decryption key. Once analysis, compliance, or any other use case requires encrypted data, it must be converted back to plaintext that can compromise security.
It addresses the core weakness by allowing the analysis of data in its ciphertext form. An early homomorphic encryption innovator, Craig Gentry, described the process by manipulating a locked box’s contents through gloves that are accessed through ports outside of the box.
It’s difficult for a third party to access locked content or what another party is working on. The box is returned to the controller once the processor has completed the assigned task, and custody is intact.
Gentry’s dissertation made homomorphic encryption achievable, but there’s a significant barrier, which is computational overhead. Processing ciphertext makes a lot of overhead as the calculations are done bit by bit. IBM claims that it has improved processing overhead and now runs 75 times faster than before. A broader range of alternative schemes has notably improved processing speeds.
In this article, see the role of big data in healthcare and look at the new healthcare dynamics. Big Data is creating a revolution in healthcare, providing better outcomes while eliminating fraud and abuse, which contributes to a large percentage of healthcare costs.
‘Data is the new science. Big Data holds the key answers’ - Pat Gelsinger The biggest advantage that the enhancement of modern technology has brought
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
We need no rocket science in understanding that every business, irrespective of their size in the modern-day business world, needs data insights for its expansion. Big data analytics is essential when it comes to understanding the needs and wants of a significant section of the audience.
Even though Big data is into main stream of operations as of 2020, there are still potential issues or challenges the researchers.