How can Homomorphic Encryption resolve Big Data's problems?

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 and its Limitations

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.

Real-life Applications of Homomorphic Encryption

Homomorphic encryption is capable of providing a mechanism for the life science industry to keep protecting intellectual property while leveraging the collaborative benefits from COVID-19 in other medical research.

Its use cases will also be obliged for financial services, where data analytics defines algorithms' success or failure is becoming increasingly essential as relative high-frequency trading advantages become more difficult. National security and critical infrastructure also offers early compelling use cases.

There will be new opportunities for data controllers who have custody of data to engage with data processors and collaborative opportunities where the parties are both the controllers and processors of data at the same time. Collaborative opportunities provide the benefits of specialisation and promise of 'data collectives' where members can define terms of use and disclosed outputs to its members.

Data collectives are an old concept to security markets. For instance, the US Securities and Exchange Commission in 2005 regulated that the security markets have to collaborate to disseminate consolidated information on quotations and transactions in security markets.

Today, homomorphic encryption could empower competitive financial organisations to provide alternatives to these sources and innovate collectively to create their own proprietary market data products.

Privacy requires being re-examined

Many initiatives attempting to harness big data's power have struggled with resource limitations, emerging technologies, and regulations. For instance, financial regulators struggle with the burdens of monitoring financial audit trails across several markets, asset types, and participants.

Although aggregating and disseminating data to regulators is crucial for surveillance, it creates a treasure trove of highly sensitive unprotected data during processing, and this happens across multiple regulators.

This big data problem and the risk could be used to manipulate trading or even shake financial markets will only keep growing unless encryption is deployed throughout the data's life cycle. Moreover, regulators only need audit trails related to red flags that their monitoring algorithms identify that can all be done in an entirely encrypted format.

The growing concerns of privacy regulation and data analytics value is another issue that the healthcare industry has been struggling with.